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| A Practical Radial Basic Function Network and Its Applications | |
| 作者姓名： | 杨绍青 贾传荧 马乐梅 |
| 作者单位： | Yang Shaoqing 1，2，Jia Chuanying 1 & Ma Lemei 2 1. Navigation Dynamic Simulation & Control Lab.，Dalian Maritime University，Dalian 116001，P. R. China 2. The Second Department，Dalian Naval Academy，Dalian 116018，P. R. China |
| 基金项目： | ThisprojectwassupportedbytheDoctorateFoundationofLiaoningProvince (2 0 0 110 2 0 95 ) |
| 摘 要： | 1 .INTRODUCTIONRecently ,artificialneuralnetworks (ANNs)havebe comeimportanttoolsinmanyfields,suchasstatisti caldataanalysis,patternrecognition ,signalprocess ing,automaticcontrol,forecastingandartificialin telligence.Thebasicproblemintheabovefieldsisfunctionapproximation .Atpresent,therearetwomainmethodsforfunctionestimation ,oneisthetra ditionalmethodnamed“parametricapproach”or“model basedapproach”andtheotherisANNmethodnamed“nonparametricapproach”foritsmanygeneralproperties.With… |
A Practical Radial Basic Function Network and Its Applications |
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| Yang Shaoqing ,Jia Chuanying & Ma Lemei . Navigation Dynamic Simulation & Control Lab.,Dalian Maritime University,Dalian ,P. R. China.A Practical Radial Basic Function Network and Its Applications[J].Journal of Systems Engineering and Electronics,2003,14(2). | |
| Authors: | Yang Shaoqing Jia Chuanying & Ma Lemei Navigation Dynamic Simulation & Control Lab Dalian Maritime University Dalian P R China |
| Institution: | 1. Navigation Dynamic Simulation & Control Lab., Dalian Maritime University,Dalian 116001, P. R. China;The Second Department, Dalian Naval Academy, Dalian 116018, P. R. China 2. Navigation Dynamic Simulation & Control Lab., Dalian Maritime University,Dalian 116001, P. R. China 3. The Second Department, Dalian Naval Academy, Dalian 116018, P. R. China |
| Abstract: | Artificial neural networks (ANNs) have become important tools in many fields, such as pattern recognition, signal processing and artificial intelligence. But many ANNs have their defects. For example, the results of RBF networks greatly rely on the parameters of the basic functions and the squared errors often disperse if the parameters are not properly selected when the gradient descent algorithm is used. This paper describes a new algorithm for training an RBF network. In function approximation, this algorithm has two main advantages: high accuracy and stable learning process. In addition, it can be used as a good classifier in pattern recognition. |
| Keywords: | RBF Network Chaos Classification |
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