• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.32-39
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• 2004

Biological neurons generally have chaotic characteristics for permanent or transient period. The effects of chaotic response of biological neuron have not yet been verified by using analytical methods. Even though the transient chaos of neuron could be beneficial to overcoming the local minimum problem, the permanent chaotic response gives adverse effect on optimization problems in general. To solve optimization problems, which are needed in almost all neural network applications such as pattern recognition, identification or prediction, and control, the neuron should have one stable fixed point. In this paper, the dynamic characteristics of the chaotic dynamic neuron and the condition that produces the chaotic response are analyzed, and the convergence conditions are presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.242-248
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• 2007

This paper analyzes on the chaos characteristics of the chaotic neural networks and presents the convergence condition. Although the transient chaos of neural network sould be beneficial to overcome the local minimum problem and speed up the learning, the permanent chaotic response gives adverse effect on optimization problems and makes neural network unstable in general. This paper investigates the dynamic characteristics of the chaotic neural networks with the chaotic dynamic neuron, and presents the convergence condition for stabilizing the chaotic neural networks.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.5
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• pp.46-55
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• 2000

This paper presents a direct adaptive control of robot system using chaotic neural networks and PD controller. The chaotic neural networks have robust nonlinear dynamic characteristics because of the sufficient nonlinearity in neuron itself, and the additional self-feedback and inter-connecting weights between neurons in same layer. Since the structure and the learning method are not appropriate for applying in control system, this neural networks have not been applied. In this paper, a modified chaotic neural networks is presented for dynamic control system. To evaluate the performance of the proposed neural networks, these networks are applied to the trajectory control of the three-axis PUMA robot. The structure of controller consists of PD controller and chaotic neural networks in parallel for conforming the stability in initial learning phase. Therefore, the chaotic neural network controller acts as a compensating controller of PD controller.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.44-52
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• 2001

This paper describes an off-line method for recognizing totally unconstrained handwritten digits using modified chaotic neural networks(MCNN). The chaotic neural networks(CNN) is modified to be a useful network for solving complex pattern problems by enforcing dynamic characteristics and learning process. Since the MCNN has the characteristics of highly nonlinear dynamics in structure and neuron itself, it can be an appropriate network for the robust classification of complex handwritten digits. Digit identification starts with extraction of features from the raw digit images and then recognizes digits using the MCNN based classifier. The performance of the MCNN classifier is evaluated on the numeral database of Concordia University, Montreal, Canada. For the relative comparison of recognition performance, the MCNN classifier is compared with the recurrent neural networks(RNN) classifier. Experimental results show that the classification rate is 98.0%. It indicates that the MCNN classifier outperforms the RNN classifier as well as other classifiers that have been reported on the same database.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.396-401
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• 2006

It is well-known that a neural network with cyclic connections generates plural limit cycles, thus, being used as a memory system for storing large number of dynamic information. In this paper, a continuous-time cyclic connection neural network was built so that each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$. The type and the number of limit cycles generated by such network has also been demonstrated through simulation. In particular, the effect of chaos signal for transition between limit cycles has been tested. Furthermore, it is evaluated whether the chaotic noise is more effective than random noise in the process of the dynamical neural networks.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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• pp.551-560
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• 2004

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series), A comparative analysis reveals that the proposed SOFPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literatures.