Contents of Volume 28 (2018)

1/2018 2/2018 3/2018 4/2018

5/2018

  • [1] Sarigul M., Avci M. (Turkey)
    Q Learning Regression Neural Network, 415-431

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.023

    Abstract: In this work, a Nadaraya-Watson kernel based learning system which owns general regression neural network topology is adapted to Q learning method to evaluate a quick and efficient action selection policy for reinforcement learning problems. By means of the proposed method Q value function is generalized and learning speed of Q agent is accelerated. The training data of the developed neural network are obtained by a standard Q learning agent on closed-loop simulation system. The efficiency of the proposed method is tested on popular reinforcement learning benchmarks and its performance is compared with other popular regression methods and Q-learning utilized methods. QLRNN increased the learning performance and it learns faster than other methods on selected benchmarks. Test results showed the eciency and the importance of the proposed network.

  • [2] Manikandan G., Sakthi U. (India)
    Optimal cluster based key management system using signcryption algorithm for wireless sensor networks, 433-455

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.024

    Abstract: Key management system maintains the confident of secret information from unauthorized users and verifying the integrity of exchanged messages and authenticity. But recent advances in electronics and computer technologies create the complexity of key management in wireless sensor networks (WSN). Additionally, the traditional key management systems are not up to the mark due to limited resources like memory, and energy constraints.In this paper, we propose an optimal cluster based key management system (OC-KMS) for WSNs. The proposed system consist of two contributions, in first, we perform the energy efficient clustering using modified animal Diaspora (MAD)optimization algorithm and cluster head (CH) selection using JAYA trust model. In second contribution, we propose the certificate less signcryption algorithm, which generates and distributes the public and private keys for each node in sensor networks. The proposed system resists various network layer attacks without affecting the network performance. The simulation resultdescribes that the proposed system perform very efficient than existing in terms of both performance and security wise.

  • [3] Saha R., Roy Chowdhury A., Banerjee S., Chatterjee T. (India)
    Detection of retinal abnormalities using machine learning methodologies, 457-471

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.025

    Abstract: This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naive Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naive Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13% and the classification accuracy of single layer perceptron for bright lesion class is 95.13% with optimal feature set.

  • [4] Provinský P. (CZ)
    Floppy logic – instructions for use , 473-494

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.026

    Abstract: This article provides a simple and practical tutorial on how to use floppy logic. The floppy logic is a method suitable for systems control and description. It preserves the simplicity of the fuzzy logic and the accuracy of the probability theory. The floppy logic allows to work consistently and simultaneously with data in the form of exact numbers, probability distributions and fuzzy sets.


4/2018

  • [1] Upasani N., Om H. (India)
    Optimized Fuzzy min-max neural network: an efficient approach for supervised outlier detection , 285-303

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.017

    Abstract: Fuzzy min-max neural network (FMN), proposed by Simpson is a well-known supervised neuro-fuzzy classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in a `fine-grained' manner that reduces its performance for pattern recognition in terms of the recall time per pattern. In this paper, we adapt the basic architecture of the FMN to represent the learned knowledge in a compact way that is in a `coarse-grained' manner, which is closed to human thinking. The working of the proposed method that is fuzzy min-max neural network with knowledge compaction (FMN-KC) is illustrated using the Fisher Iris dataset. The potential of using the FMN-KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup 99 dataset available in UCI repository. The proposed method achieves around 50% gain in the recall time as compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture FMN-KC for supervised outlier detection in the real time applications, where recall time per pattern is one of the key parameters.

  • [2] Liu Y., Cai K., Liu C., Zheng F. (China)
    CSRNCVA: A model of cross-media semantic retrieval based on neural computing of visual and auditory sensations , 305-323

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.018

    Abstract: Cross-media semantic retrieval (CSR) and cross-modal semantic mapping are key problems of the multimedia search engine. The cognitive function and neural structure for visual and auditory information process are an important reference for the study of brain-inspired CSR. In this paper, we analyze the hierarchy, the functionality and the structure of visual and auditory in the brain. Considering an idea from deep belief network and hierarchical temporal memory, we presented a brain-inspired intelligent model, called cross-media semantic retrieval based on neural computing of visual and auditory sensation (CSRNCVA). Algorithms based on CSRNCVA were developed. It employs belief propagation algorithms of probabilistic graphical model and hierarchical learning. The experiments show that our model and algorithms can be effectively applied to the CSR. This work provides an important significance for brain-inspired cross-media intelligence framework.

  • [3] Chang G., Huo H. (China)
    A method of fine-grained short text sentiment analysis based on machine learning , 325-344

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.019

    Abstract: Text sentiment analysis plays an important role in social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the in fluence of sentiment words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on machine learning. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.

  • [4] Xu Y., He M. (China)
    Improved artificial neural network based on intelligent optimization algorithm , 345-360

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.020

    Abstract: Neural network based on back-propagation (BP) algorithm is a widely used prediction model. However, the nodes number of the first hidden layer, the learning rate and momentum factor are usually determined manually, which affects the forecast accuracy of network. Therefore, in this paper, to improve the forecast accuracy, firstly, the nodes number of the first hidden layer is selected adaptively based on minimizing mean square error (MSE). Secondly, improved genetic algorithm (GA) is proposed to train the learning rate and momentum factor dynamically, which includes multi-point crossover and single point mutation. Thirdly, we construct a new neural network model based on the adaptively selected nodes number of the first hidden layer, the dynamically selected learning rate and momentum factor, which is called HN-GA-BP neural network model. Finally, the proposed neural network model is used to forecast the carbon dioxide contents in China for fifty years. Experimental results demonstrate the effectiveness of the proposed HN-GA-BP neural network model.

  • [5] Balal E., Cheu R.L. (U.S.)
    Comparative evaluation of fuzzy inference system, support vector machine and multilayer feed-forward neural network in making discretionary lane changing decisions , 361-378

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.021

    Abstract: This paper compares Fuzzy Inference System (FIS), Support Vector Machine (SVM) and MultiLayer Feed-forward neural network (MLF) in modeling a driver's decision when making a discretionary lane changing move on a freeway. The FIS model has been developed and published in an earlier work by the authors, whereas the SVM and MLF models are newly developed in this research. The FIS, SVM and MLF models use the same four inputs: the gap between the subject vehicle and the leading vehicle in the original lane, the gap between the subject vehicle and the leading vehicle in the destination lane, the gap between the subject vehicle and the trailing vehicle in the destination lane, and the distance between the preceding and trailing vehicles in the destination lane. The models give a binary decision of "no, stay in the same lane" or "yes, move to the destination lane now". These models were trained and then tested with the Next Generation SIMulation (NGSIM) vehicle trajectory data. The results have shown that the FIS has the highest accuracies in making correct lane changing decisions. It recommends "yes, move to the destination lane now" with 82.2% accuracy, and "no, stay in the same lane" with 99.5% accuracy. The SVM model also outperformed the traditional gap acceptance model which was used as the benchmark. However, the MLF model was not as accurate as the gap acceptance model.

  • [6] Garlík B. (CZ)
    Application of neural networks and evolutionary algorithms to solve energy optimization and unit commitment for a smart city , 379-413

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.022

    Abstract: The optimization problem of two or more special-purpose functions of the energy system is subjected to an analysis. Based on experience of our research and general knowledge of partial solutions of energy system optimization at the level of control of production and power energy supply by energy companies in the Czech Republic, a special-purpose (cost) function has been defined. By analysing the special-purpose function, penalty and limitations have been defined. Using the fuzzy logic, a set of suitable solutions for the special-purpose function is accepted. An optimum of the special-purpose function is looked for using the simulated annealing method. The history of electricity consumption is sorted by day and by hour, representing the multidimensional data. When using the cluster analysis, type daytime diagrams of consumption are defined. Type daytime diagrams form prototypes of identified clusters. The so-called self-organizing neural network with Kohonen map attached is used to perform the cluster analysis. The result of our research is presented by an experiment.


3/2018

  • [1] Z. Tian, G. Wang, Y. Ren, S. Li, Y. Wang (China)
    An adaptive online sequential extreme learning machine for short-term wind speed prediction based on improved artificial bee colony algorithm , 191-212

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.012

    Abstract: As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction performance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

  • [2] A. Kayabasi, A. Toktas, K. Sabanci, E. Yigit (Turkey)
    Automatic classification of agricultural grains: comparison of neural networks, 213-224

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.013

    Abstract: In this study, applications of well-known neural networks such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) for wheat grain classification into three species are comparatively presented. The species of wheat grains which are Kama (#70), Rosa (#70) and Canadian (#70) are designated as outputs of neural network models. The classification is carried out through data of wheat grains (#210) acquired using X-ray technique. The data set includes seven grain's geometric parameters: Area, perimeter, compactness, length, width, asymmetry coefficient and groove length. The neural networks input with the geometric parameters are trained through 189 wheat grain data and their accuracies are tested via 21 data. The performance of neural network models is compared to each other with regard to their accuracy, efficiency and convenience. For testing data, the ANN, ANFIS and SVM models numerically calculate the outputs with mean absolute error (MAE) of 0.014, 0.018 and 0.135, and classify the grains with accuracy of 100 %, 100% and 95.23 %, respectively. Furthermore, data of 210 grains is synthetically increased to 3210 in order to investigate the proposed models under big data. It is seen that the models are more successful if the size of data is increased, as well. These results point out that the neural networks can be successfully applied to classification of agricultural grains whether they are properly modelled and trained.

  • [3] P. Moos, M. Svítek, M. Novák, Z. Votruba (CZ)
    Information model of resonance phenomena in brain neural networks, 225-239

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.014

    Abstract: The paper presents an information model for representation of brain linear and nonlinear resonance phenomena based on information nullors. In the brain functions the rhythms and quasi periodicity of processes in neural networks play the outstanding (significant) role. It is why adaptive resonance theory (ART) including resonant effects has been studied for a long time by many authors. The periodicity in the transfers of signals between the long-term memory (LTM) and short-term memory (STM) creates a possibility of resonance system structure. LTM with information content representing expectations and STM covering sensory information in resonance process offer effective learning. Nonlinear adaptive resonance creates conditions for new knowledge, or inventory observation. In the paper this feature is newly modelled by an information gyrator that best fits these linear and non-linear phenomena.

  • [4] L. Zaorálek, J. Platoš, V. Snášel (CZ)
    Patient-adapted and inter-patient ECG classification using neural network and gradient boosting , 241-254

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.015

    Abstract: Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.

  • [5] H. Nabovati, H. Haleh, B. Vahdani (Iran)
    Fuzzy multi-objective optimization algorithms for solving multi-mode automated guided vehicles by considering machine break time and artificial neural network, 255-283

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.016

    Abstract: In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine's breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called “fuzzy multi-objective invasive weeds optimization algorithm" and “fuzzy multi-objective cuckoo search algorithm" with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms.


2/2018

  • [1] Stefanovic P., Kurasova O. (Lithunia)
    Outlier detection in self-organizing maps and their quality estimation , 105-117

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.006

    Abstract: In the paper, an algorithm that allows to detect and reject outliers in a self-organizing map (SOM) has been proposed. SOM is used for data clustering as well as dimensionality reduction and the results obtained are presented in a special graphical form. To detect outliers in SOM, a genetic algorithm-based travelling salesman approach has been applied. After outliers are detected and removed, the SOM quality has to be estimated. A measure has been proposed to evaluate the coincidence of data classes and clusters obtained in SOM. A larger value of the measure means that the distance between centers of different classes in SOM is longer and the clusters corresponding to the data classes separate better. With a view to illustrate the proposed algorithm, two datasets (numerical and textual) are used in this investigation.

  • [2] Hlavác V. (CZ)
    Genetic programming with either stochastic or deterministic constant evaluation, 119-131

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.007

    Abstract: Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.

  • [3] Li F., Zurada J.M., Wu W. (China,PL)
    Sparse representation learning of data by autoencoders with L1/2 regularization , 133-147

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.008

    Abstract: Autoencoder networks have been demonstrated to be effcient for unsupervised learning of representation of images, documents and time series. Sparse representation can improve the interpretability of the input data and the generalization of a model by eliminating redundant features and extracting the latent structure of data. In this paper, we use L1/2 regularization method to enforce sparsity on the hidden representation of an autoencoder for achieving sparse representation of data. The performance of our approach in terms of unsupervised feature learning and supervised classiffcation is assessed on the MNIST digit data set, the ORL face database and the Reuters-21578 text corpus. The results demonstrate that the proposed autoencoder can produce sparser representation and better reconstruction performance than the Sparse Autoencoder and the L1 regularization Autoencoder. The new representation is also illustrated to be useful for a deep network to improve the classiffcation performance.

  • [4] Huang JP., Wang XA., Zhao Y., Xin C., Xiang H. (China)
    Large earthquake magnitude prediction in Taiwan based on deep learning neural network, 149-160

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.009

    Abstract: In this paper, a deep learning-based method for earthquake prediction is proposed. Large-magnitude earthquakes and tsunamis triggered by earthquakes can kill thousands of people and cause millions of dollars worth of economic losses. The accurate prediction of large-magnitude earthquakes is a worldwide problem. In recent years, deep learning technology that can automatically extract features from mass data has been applied in image recognition, natural language processing, object recognition, etc., with great success. We explore to apply deep learning technology to earthquake prediction. We propose a deep learning method for continuous earthquake prediction using historical seismic events. First, we project the historical seismic events onto a topographic map. Taking Taiwan as an example, we generate the images of the dataset for deep learning and mark a label "1" or "0", depending on whether in the upcoming 30 days a greater than M6 earthquake will occur. Second, we train our deep leaning network model, using the images of the dataset. Finally, we make earthquake predictions, using the trained network model. The result shows that we can get the best result, when we predict earthquakes in the upcoming 30 days using data from the past 120 days. Here, we use R score as the performance metrics. The best R score is 0.303. Although the R score is not high enough, using the past 120 days' historic seismic event to predict the upcoming 30 days' biggest earthquake magnitude can be seen as the pattern of Taiwan earthquake because the R score is rather good compared to other datasets. The proposed method performs well without manually designing feature vectors, as in the traditional neural network method. This method can be applied to earthquake prediction in other seismic zones.

  • [5] Cavuslu M.A., Sahin S. (Turkey)
    FPGA implementation of ANN training using Levenberg and Marquardt algorithms , 161-178

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.010

    Abstract: Artificial Neural Network (ANN) training using gradient-based Levenberg & Marquardt (LM) algorithm has been implemented on FPGA for the solution of dynamic system identification problems within the scope of the study. In the implementation, IEEE 754 floating-point number format has been used because of the dynamism and sensitivity that it has provided. Mathematical approaches have been preferred to implement the activation function, which is the most critical phase of the study. ANN is tested by using input-output sample sets, which are shown or not shown to the network in the training phase, and success rates are given for every sample set. The obtained results demonstrate that implementation of FPGA-based ANN training is possible by using LM algorithm and as the result of the training, the ANN makes a good generalization.

  • [6] Ruzek M. (CZ)
    Homeostatic learning rule for artificial neural networks , 179-189

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.011

    Abstract: This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to 2nd such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept { the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.


1/2018

  • [1] Vareka L., Mautner P. (CZ)
    Modifications of unsupervised neural networks for single trial P300 detection , 1-16

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.001

    Abstract: P300 brain-computer interfaces (BCIs) have been gaining attention in recent years. To achieve good performance and accuracy, it is necessary to optimize both feature extraction and classification algorithms. This article aims at verifying whether supervised learning models based on self-organizing maps (SOM) or adaptive resonance theory (ART) can be useful for this task. For feature extraction, the state-of-the-art Windowed means paradigm was used. For classification, proposed classifiers were compared with state-of-the-art classifiers used in BCI research, such as Bayesian Linear Discriminant Analysis, or shrinkage LDA. Publicly available datasets from fifteen healthy subjects were used for the experiments. The results indicated that SOM-based models yield better results than ART-based models. The best performance was achieved by the LASSO model that was comparable to state-of-the-art BCI classifiers. Further possibilities for improvements are discussed.

  • [2] Horaisova K., Dudasova J., Kukal J., Rusina R., Matej R., Buncova M. (CZ)
    Discrimination between Alzheimer’s disease and amyotrophic lateral sclerosis via affine invariant spherical harmonics analysis of SPECT images , 17-39

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.002

    Abstract: Alzheimer's Disease (AD) is the most frequent form of degenerative dementia and its early diagnosis is essential for effective treatment. Functional imaging modalities including Single Photon Emission Computed Tomography (SPECT) are often used with such an aim. However, conventional evaluation of SPECT images relies on manual reorientation and visual evaluation of tomographic slices which is time consuming, subjective and therefore prone to error. Our aim is to show an automatic Computer-Aided Diagnosis (CAD) system for improving the early detection of the AD. For this purpose, ane invariant descriptors of 3D SPECT image can be useful. The method consists of four steps: evaluation of invariant descriptors obtained using spherical harmonic analysis, statistical testing of their significance, application of regularized binary index models, and model verification via leave-one-out cross-validation scheme. The second approach is based on Support Vector Machine (SVM) classifier and visualization with use of self-organizing maps. Our approaches were tested on SPECT data from 11 adult patients with definite Alzheimer's disease and 10 adult patients with Amyotrophic Lateral Sclerosis (ALS) who were used as controls. A significant difference between SPECT spherical cuts of AD group and ALS group was both visually and numerically evaluated.

  • [3] Ebadati E., Mortazavi T. (Iran)
    An efficient hybrid machine learning method for time series stock market forecasting , 41-55

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.003

    Abstract: Time series forecasting, such as stock price prediction, is one of the most important complications in the financial area as data is unsteady and has noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method, the output values are further fed to a developed ANN algorithm to fix errors on exact point. The analysis suggests that the GA and ANN can increase the accuracy in fewer iterations. The analysis is conducted on the 200-day main index, as well as on five companies listed on the NASDAQ. By applying the proposed method to the Apple stocks dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms, the proposed method reaches to 99.99% improvement in SSE and 90.66% in time improvement, in comparison to traditional methods. These results show the performances and the speed and the accuracy of the proposed approach.

  • [4] Ramos A.D., López-Rubio E., Palomo E.J. (Spain-Equador)
    The role of the lattice dimensionality in the self-organizing map, 57-86

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.004

    Abstract: The Self-Organizing Map model considers the possibility of 1D and 3D map topologies. However, 2D maps are by far the most used in practice. Moreover, there is a lack of a theory which studies the relative merits of 1D, 2D and 3D maps. In this paper a theory of this kind is developed, which can be used to assess which topologies are better suited for vector quantization. In addition to this, a broad set of experiments is presented which includes unsupervised clustering with machine learning datasets and color image segmentation. Statistical significance tests show that the 1D maps perform significantly better in many cases, which agrees with the theoretical study. This opens the way for other applications of the less popular variants of the self-organizing map.

  • [5] Peng Z., Jiang Y., Yang X., Zhao Z., Zhang L., Wang Y. (China)
    Bus arrival time prediction based on PCA-GA-SVM, 87-104

      Full text     DOI: http://dx.doi.org/10.14311/NNW.2018.28.005

    Abstract: Considering the correlations of the input indexes and the deficiency of calibrating kernel function parameters when support vector machine (SVM) is applied, a forecasting method based on principal component analysis-genetic algorithm-support vector machine (PCA-GA-SVM) is proposed to improve the precision of bus arrival time prediction. And the No. 232 bus in Shenyang City of China is taken as an example. The traditional SVM and Kalman Filtering model and GA-SVM are also employed to make comparative analysis on the prediction rate, respectively. The result indicates that PCA-GA-SVM obtains more accurate prediction results of bus arrival time prediction.