AN IMPROVED CONTROLLED CHAOTIC NEURAL NETWORK FOR PATTERN RECOGNITION

International Journal of Mathematical Modelling & Computations

مقاله 6، دوره 4، 3 (SUMMER)، فروردین 2014، صفحه 267-276   

نویسندگان

Maryam Nahvi Farsi؛ Majid Amirfakhrian؛ Alireza Vasiq

Iran, Islamic Republic of

چکیده

A sigmoid function is necessary for creation a chaotic neural network (CNN). In this paper, a new function for CNN is proposed that it can increase the speed of convergence. In the proposed method, we use a novel signal for controlling chaos. Both the theory analysis and computer simulation results show that the performance of CNN can be improved remarkably by using our method. By means of this control method, the outputs of the controlled CNN converge to the stored patterns and they are dependent on the initial patterns. We observed that the controlled CNN can distinguish two initial patterns even if they are slightly different. These characteristics imply that the controlled CNN can be used for pattern recognition.
 

کلیدواژه‌ها

Chaotic Neural Network؛ Controlling Chaos؛ Associative Memory