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On-Line Training with Guide Data: Shall We Select the Guide Data Randomly or Based on Cluster Centers?

On-Line Training with Guide Data: Shall We Select the Guide ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Univ Aizu Dept Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781509042401

To retrain an existing multilayer perceptron (MLP) on-line using newly observed data, it is necessary to incorporate the new information while preserving the performance of the network. This is known as the "plasticitystability" problem. For this purpose, we proposed an algorithm for on-line training with guide data (OLTA-GD). OLTA-GD is good for implementation in portable/wearable computing devices (P/WCDs) because of its low computational cost, and can make us more independent of the internet. Results obtained so far show that, in most cases, OLTA-GD can improve an MLP steadily. One question in using OLTA-GD is how we can select the guide data more efficiently. In this paper, we investigate two methods for guide data selection. The first one is to select the guide data randomly from a candidate data set G, and the other is to cluster G first, and select the guide data from G based on the cluster centers. Results show that the two methods do not have significant difference in the sense that both of them can preserve the performance of the MLP well. However, if we consider the risk of "instantaneous performance degradation", random selection is not recommended. In other words, cluster center-based selection can provide more reliable results for the user during on-line training.

关键词： Decision Boundary Making Multilayer Perceptron backpropagation algorithm On-Line Learning Guide Data

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Optimized Neural Network for Improved Electrocardiogram Beat Classification 6

Optimized Neural Network for Improved Electrocardiogram Beat...

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IEEE 6th International Advance Computing Conference (IACC)

作者： Dash, Sanjit K. Rao, G. Sasibhushana Raajdhani Engn Coll Dept ECE Bhubaneswar Orissa India Andhra Univ Coll Engn Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781467382861

The performance of neural network as a classifier depends on several factors such as initialization of weights, its architecture, between class imbalance in the dataset, activation function etc. Though a three layered neural network is able to approximate any non-linear function, number of neurons in the hidden layer plays a significant roll in the performance of the classifier. In this study, the importance of the number of hidden layer neurons of neural network is analyzed for the classification of ECG signals. Five different arrhythmias and the normal beat are classified for different number of hidden layer neurons to examine the performance of the classifier. In this study we get the best number as 35. After the training of the neural network with the optimized number of neurons in the hidden layer, we have tested the performance with three different datasets. The average sensitivity, specificity and accuracy achieved is 94.91%, 99.69% and 99.46% respectively.

关键词： Optimized Artificial Neural Network ECG Arrhythmia Multiclass Pattern Classification backpropagation algorithm

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Use Multilayer Perceptron in Calibrating Multistage Non-linearity of Split Pipelined-ADC

Use Multilayer Perceptron in Calibrating Multistage Non-line...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Zhang, Tianli Cao, Yuefeng Ye, Fan Ren, Junyan Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China

ISBN: (纸本)9781538648810

A split-based background calibration technique for pipelined-ADC is proposed in this brief to handle both capacitors' mismatches and non-linearity of residue amplifiers in multiple pipeline stages. Some concepts and approaches of machine learning, say multilayer perceptron and backpropagation algorithm, are introduced to deal with the problems appearing in modeling and solving the nonlinear calibration filter. Computer simulations demonstrate an exaltation of both SNDR and SFDR for more than 50dB in a 15-bit 7-stages pipelined-ADC with non-ideal first three stages.

关键词： pipelined ADC background calibration split ADC machine learning multilayer perceptron backpropagation algorithm

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Neural Network & Genetic algorithm Based Approach to Network Intrusion Detection & Comparative Analysis of Performance

Neural Network & Genetic Algorithm Based Approach to Network...

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15th International Conference on Computer and Information Technology (ICCIT)

作者： Pal, Biprodip Hasan, Md. Al Mehedi Rajshahi Univ Engn & Technol Dept Comp Sci & Engn Rajshahi Bangladesh

ISBN: (纸本)9781467348362;9781467348331

In this paper backpropagation learning algorithm and genetic algorithm is applied for network intrusion detection and also to classify the detected attacks into proper types. During the training process of the backpropagation algorithm two possible set of features in the rule sets are used separately to determine proper rule set features for better performance. Then the performance of genetic algorithm is compared to the performance of both of the backpropagation approach. The process is tested on training dataset as well as test dataset to analyze the performance. It is found that in detecting the attack connections backpropagation algorithm shows better performance but in classifying the detected attacks into proper types the genetic algorithm approach is more successful.

关键词： backpropagation algorithm Genetic algorithm Intrusion detection Security

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Optimization of type-2 Fuzzy Weight for Neural Network using Genetic algorithm and Particle Swarm Optimization

Optimization of type-2 Fuzzy Weight for Neural Network using...

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World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479914159

In this paper two bio-inspired methods are applied to optimize the type-2 fuzzy inference systems used in the neural network with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the learning method architecture and the adaptation of type-2 fuzzy weights are presented. In this work an optimized type-2 fuzzy inference systems to manage weights for the neural network and the results for the two bio-inspired methods are presented. The proposed approach is applied to a case of time series prediction, specifically in Mackey-Glass time series.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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Neural Networks for Prediction of Oil Production

Neural Networks for Prediction of Oil Production

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18th International-Federation-of-Automatic-Control (IFAC) Conference on Technology, Culture and International Stability (TECIS)

作者： Muradkhanli, Leyla Baku Higher Oil Sch Baku Azerbaijan

Artificial neural networks are popular tool in oil and gas industry. The objective of this paper is to develop an artificial neural network that predicts oil production in State Oil Company of Azerbaijan Republic (SOCAR). Multilayer perceptron neural network is applied to predict oil production. As a result 99 percent accuracy in training procedure is achieved. The accuracy of the developed model was compared with empirical correlations. Great matching between neural network and actual data has been found. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Artificial neural network Multilayer perceptron Neural network model Training backpropagation algorithm Root means square error

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Modification of Learning Feedforward Neural Networks with the BP Method 20th

Modification of Learning Feedforward Neural Networks with th...

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Bilski, Jaroslaw Smolag, Jacek Najgebauer, Patryk Czestochowa Tech Univ Dept Comp Engn Al Armii Krajowej 36 PL-42200 Czestochowa Poland

ISBN: (纸本)9783030879860;9783030879853

The backpropagation (BP) algorithm is a worldwide used method for learning neural networks. The BP has a low computational load. Unfortunately, this method converges relatively slowly. In this paper a new approach to the backpropagation algorithm is presented. The proposed solution speeds up the BP method by using vector calculations. This modification of the BP algorithm was tested on a few standard examples. The obtained performance of both methods was compared.

关键词： Feedforward neural network Neural network learning algorithm backpropagation algorithm Parallel computation

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Artificial Neural Networks Model to Improve the Performance Index of the Coil-Disc Assembly in Tube Flow 1st

Artificial Neural Networks Model to Improve the Performance ...

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1st International Conference on Computational Intelligence and Informatics (ICCII)

作者： Durga, R. Kanaka Kumar, C. Srinivasa Mohan, Vaka Murali Kumar, L. Praveen Prasad, P. Rajendra VIGNAN Inst Technol Sci Dept CSE Hyderabad TS India TRR Coll Engn Dept CSE Hyderabad TS India Andhra Univ AUCE Dept Chem Engn Visakhapatnam AP India

ISBN: (纸本)9789811024719;9789811024702

An artificial neural networks model to enhance the performance index of mass transfer function in tube flow by means of entry region coil-disc assembly promoter was inserted coaxially is presented in this paper. Popular backpropagation algorithm was utilized to test, train and normalize the network data to envisage the performance of mass transfer function. The experimental data of the study is separated into two sets one is training sets and second one is validation sets. The 248 sets of the experimental data were used in training and 106 sets for the validation of the artificial neural networks using MATLAB 7.7.0, particularly tool boxes to predict the performance index of the mass transfer in tube for faster convergence and accuracy. The weights were initialized within the range of [-1, 1]. The network limitations in all attempts taken learning rate as 0.10 and momentum term as 0.30. The finest model was selected based on the MSE, STD and R2. In this, network with 5_8_1 configuration is recommended for mass transfer training. This research work reveals that artificial neural networks with adding more number of layers and nodes in hidden layer may not increase the performance of mass transfer function.

关键词： Artificial neural network Mass transfer Coil-disc assembly Tube backpropagation algorithm

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Bank credit authorization using fuzzy expert system 12th

Bank credit authorization using fuzzy expert system

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12th International Conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Menekay, Mustafa Near East Univ Dept Int Business POB 99138 TR-10 North Cyprus Mersin Turkey

Companies Financial and economic performance is determined by analyzing their financial statements, that includes such important information as growth in sales, return to stockholders, profit margins, return on equity and credit risks. This information is useful for investors, creditors and other external users. To analyze financial condition of company and make decision need more professionalism and good economical and financial background from managers. In many cases financial manager make decision in condition of uncertainty. Economical instability, political situation needs many companies to work in condition of uncertainty, fuzziness of information. In this paper we take a closer look how information in the financial statement can be combined, analyzed and used by fuzzy expert systems to support many important financial decisions in credit analysis and authorization. This is achieved by the development of knowledge base using knowledge of experienced specialists, human experts in this field. In expert system knowledge base is combined with inference engine to make final decision. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Neural network retina recognition backpropagation algorithm neural network

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Inclined Planes Optimization algorithm in Optimal Architecture of MLP Neural Networks 3

Inclined Planes Optimization Algorithm in Optimal Architectu...

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3rd International Conference on Pattern Analysis and Image Analysis (IPRIA)

作者： Shahraki, Najmeh Sayyadi Zahiri, Seyed-Hamid Univ Birjand Dept Elect & Comp Engn Birjand Iran

ISBN: (纸本)9781509064540

In this paper an Inclined Planes Optimization algorithm, is used to optimize the performance of the multilayer perceptron. Indeed, the performance of the neural network depends on its parameters such as the number of neurons in the hidden layer and the connection weights. So far, most research has been done in the field of training the neural network. In this paper, a new algorithm optimization is presented in optimal architecture for data classification. Neural network training is done by backpropagation (BP) algorithm and optimization the architecture of neural network is considered as independent variables in the algorithm. The results in three classification problems have shown that a neural network resulting from these methods have low complexity and high accuracy when compared with results of Particle Swarm Optimization and Gravitational Search algorithm.

关键词： Inclined Planes Optimization algorithm the architecture of neural network classification backpropagation algorithm

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