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During optimize the weights of Elman recurrent neural networks by genetic algorithm, if use simple genetic algorithm, two problems appear: constringency too early and the speed of search too slow in the anaphase. Aim at these problems, an improved Niche genetic algorithm is presented in this paper. Simulation results show that this algorithm can achieve(More)
This paper presents a cultural algorithm (CA) based on multilayer belief spaces that selects the best belief space from the multilayer belief spaces to guide the search of population space. The selected best belief space exploits knowledge extracted during the search to improve the performance of an evolutionary algorithm. We integrate cultural algorithm(More)
Clustering techniques have emerged as a popular choice for achieving energy efficiency and scalable performance in large scale sensor networks. In order to achieve efficient use of energy networks that extend the network time, increase the amount of data transfer, this study and reference in many previous proposed clustering algorithms, this paper(More)
In this paper, an emerging artificial neural network is proposed and researched. The differential of exciting intensity of each neuron is mutually feedback to each other in the network. Hence the overall network turns out to be a high-order nonlinear system. Besides, the iterative equations are derived by discretizing the state equations. In this way, the(More)
In this paper, a dynamical recurrent artificial neural network (ANN) is proposed and studied. Inspired from a recent research in neuroscience, we introduced nonsynaptic coupling to form a dynamical component of the network. We mathematically proved that, with adequate neurons provided, this dynamical ANN model is capable of approximating any continuous(More)
Based on the idea of combining models to improve prediction accuracy and robustness, this paper uses FCM to separate a whole training data set into several clusters with different centers. Each subset is trained by BP neural network. The degrees of membership are used for combining these models to obtain the final result. It has higher approaching precision(More)
In this contribution, a Bayes Ying-Yang(BYY) harmony based approach for on-line signature verification is presented. In the proposed method, a simple but effective Gaussian Mixture Models(GMMs) is used to represent for each user's signature model based on the prior information collected. Different from the early works, in this paper, we use the Bayes Ying(More)
Effective monitoring and early warning for cracking severity are directly related to the ethylene production stability and the overall economic benefits. Elman neural network is used to establish the early warning model of cracking severity, and an improved Cultural Differential Evolution algorithm(ICDE) is proposed to training the model. This hybrid(More)