Study On Granular Neural Networks Based On Rough Sets

Rough sets theory, as one of the three main models of granular computing, canfind hidden knowledge and reveal potential law by analyzing and reasoning on thedata directly. Therefore, it's a kind of natural data mining method. As another classicmethod of data mining, neural networks is a mathematical model for distributed andparallel information processing by imitating the behavioral characteristics ofbiological neural networks. Rough sets and neural networks have manycomplementarities in information processing, knowledge acquisition, noisesuppression capability and generalization ability. So, granular neural networksintegrated advantages of rough sets and neural networks, as a new important branch ofintelligent integrated system, has become one of hot topics in the domain of intelligentinformation processing.The dissertation researched two integrated modes of rough sets and the neuralnetworks. One was that rough sets was regarded as a front-end processor, using itsattribute reduction algorithm to compress the dimensions of information space, tosimplify the structure of the neural network, improve neural network training speedand prediction accuracy. Another was that rough sets was used to extract decisionrules to define the granular neurons, determine the structure of neural networks and itsconnection weights which achieves the seamless integration of rough sets theory andneural networks. In addition, this dissertation also studied the extreme learningalgorithm of each integrated mode, completed learning process through mathematicaltransform. The main works of this dissertation included the following aspects:1. On basis of guaranteeing the classification ability unchanged, simplify thetraining data set through attribute reduction algorithm of rough sets theory. Then, thereduced training set was used to optimize the structure of BP neural networks,accelerate its training speed, and improve its generalization ability. In view of thetraditional BP algorithm has some inherent vice, such as slow training speed, localminimum and over fitting problem, this dissertation proposed a new method todetermine adaptively weights and thresholds of granular BP neural network throughquantum-behaved particle swarm algorithm which has global search ability.2. This dissertation presented a new model of granular RBF neural networksbased on rough sets and AP clustering algorithm. In this model, AP clusteringalgorithm, which doesn't need any prior knowledge, was used to cluster the reducted data set. Then, the centers and their widths obtained by AP algorithm were transmitedto RBF units in the hidden layer of granular RBF network. After that, the outputs ofRBF units in the hidden layer were calculated, and granular RBF networks weretrained by the traditional RBF learning algorithm.3. When granular BP networks and granular RBF networks had a single hiddenlayer structure, this dissertation proposed an adaptive extreme learning algorithm tooptimize the connection weights and thresholds value. In this algorithm, AP clusteringalgorithm was used to determine adaptively the numbers of the neurons in the hiddenlayer, and obtain clustering centers and their withds which were defined the Gaussfunctions to be regarded as the new activation functions of the hidden layer.4. According to extracted decision rules through the algorithms of attributereduction and value reduction, this dissertation proposed a new granular neuralnetwork model, called rough rule granular neural networks. In this model, rulematching layer replaced the hidden layer of traditional neural networks. Each neuronof rule matching layer represented a decision rule. Input weights and output weightswere initialized according to front components and latter components of rules. Then,the output weights were adjusted further by extreme learning algorithm to improve theclassification ability of the networks.5. Considering decision rules should have the ability of fault-tolerant, thisdissertation proposed granular double neural networks and its learning algorithmbased on variable precision rough set model and extreme learining algorithm.In thismodel, neurons of the middle layer and the output layer were all granular doubleneurons which included upper approximation neuron and lower approximation neuronto represent the upper approximation and lower approximation of each rule. Finally,the output weights were adjusted further by extreme learning algorithm to improve theclassification ability of the networks. In addition, in order to improve the capability ofgranular double neural networks when processing mass data set, this dissertationproposed an optimized method based on AP clustering algorithm.The dissertation studied several granular neural networks models and theirlearing algorithms, and verified the effectiveness of these models by experiments.