LettersA novel hysteretic chaotic neural network and its applications
Introduction
Neural networks have shown to be powerful tools for solving optimization problems, particularly NP-hard problems. The electrophysiological experiments of animals have proven that chaos dynamics and hysteresis phenomena that exist in real neurons and neural networks play important roles in neuron activity [7], [17], [5], [15]. It is believed that the investigation of the dynamic characters of neural networks is helpful to understand the memory rules of the brain. Various chaotic neural networks models are proposed nowadays. Yu [21] proposed a novel approach of encryption based on chaotic neural networks with time-varying delay. Iwai et al. [11] investigated the effects of correlation among stored patterns on the associative dynamics in the chaotic neural network model. Potapov et al. [14] considered the problem of creating a robust chaotic neural network. Chen and Aihara [3] have proposed a transiently chaotic neural network (TCNN). Xu et al. [19] have presented a method of introducing several time-independent parameters into the original TCNN model. On the other hand, many hysteretic neural networks are also proposed, for instance, in [2], [8], [16], [18], [20], and their performs are better than some nonhysteretic neural networks. However, no neurons or networks have the hysteretic and chaotic properties simultaneously. In this letter, we introduce a new neuron model with the two properties simultaneously, and complex dynamic behavior is investigated. It is found that the neural network composed of the neurons has the better performance with respect to computational abilities.
Section snippets
A neuron model
The neuron model can be formulated as where x(t) denotes the output of neuron at discrete time t, I is the input bias of neuron, and y(t) is the inner state of the neuron. The activation function f( ) is composed of two offset sigmoid function, a and b are the center parameters, c1 and c2 are the shape parameters, and α is the self-feedback gain coefficient. The
Hysteretic chaotic neural network
Consider a network composed of the mentioned neurons to solve the optimization problems. The network model is described as where β is the coupling coefficient among the neurons. With β increasing, the coupling degree among neurons becomes stronger in chaotic status, and the neurons will escape from chaotic states and get into stable states. Tuning the parameter quickens the optimization rate.
Chaos searching has the ergodicity
Numerical experiments
The networks can be applied to the function optimization problem and the combinatorial optimization problem.
Conclusion
In this letter, we propose a novel neuron model whose activation function is composed of two offset sigmoid functions. By choosing appropriate parameters, the neuron can possess hysteresis property and chaos property simultaneously, and exhibits complex dynamic behavior. Utilizing the two properties, the network, consisting the neurons, can own the ability of global optimization. Complex function optimization problems and combinatorial optimization problems are investigated in this letter by
Acknowledgment
This work was supported by the National Natural Science Foundation of China (10402003).
Xiangdong Liu was born in Hubei, China, in 1971. He received the Ph.D. degree in flight vehicle design from Harbin Institute of Technology, Heilongjiang, China, in 1998. His research interests include nonlinear dynamics of complex systems and chaos synchronization.
References (21)
A methodological approach to parallel simulated annealing on an SMP system
J. Parallel Distrib. Comput.
(2002)- et al.
Chaotic simulated annealing by a neural network model with transient chaos
Neural Networks
(1995) - et al.
Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation
C.R. Acad. Sci. Paris, Sci. de la vie/Life Sci.
(2001) - et al.
Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing
Math. Comput. Model.
(2005) - et al.
Effects of correlation among stored patterns on associative dynamics of chaotic neural network
Physica D: Nonlinear Phenom.
(2005) - et al.
A simulated annealing and hill-climbing algorithm for the traveling tournament problem
Eur. J. Oper. Res.
(2006) - et al.
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Lett.
(2006) - et al.
Robust chaos in neural networks
Phys. Lett. A
(2000) - et al.
A binary Hopfield neural network with hysteresis for large crossbar packet switches
Neurocomputing
(2005) - et al.
A method to improve the transiently chaotic neural network
Neurocomputing
(2005)
Cited by (30)
Secure communication based on the synchronous control of hysteretic chaotic neuron
2017, NeurocomputingCitation Excerpt :Chaotic neural network is composed of many chaotic neurons, so it has complex coupling characteristic. Based on the conventional chaotic neural network, hysteretic chaotic neuron and neural network are proposed by changing their activation functions [22,23]. The control of the hysteretic chaotic neural network are further studied [24], and good application effects, such as associative memory and optimal computation are got.
Optimization of hysteretic chaotic neural network based on fuzzy sliding mode control
2016, NeurocomputingCitation Excerpt :In other words, chaos in the neuron or neural network is not controlled by the control theory, and the controllability of chaos in the neuron or neural network is not deeply explored. Likewise, hysteretic chaotic neural network [18,19] can be used to resolve associative memory and function optimization problems by annealing strategy. After decaying the hysteretic parameters and self-feedback weights, hysteretic chaotic neural network becomes the conventional Hopfield neural network.
Associative memory network and its hardware design
2015, NeurocomputingCitation Excerpt :The earlier studies on the associative memory focus mostly on the binary patterns. Many associative networks, such as Hopfiled neural network, bidirectional associative memory (BAM) neural network, chaotic neural network, and so on, are proposed to improve the associative performance [10–14]. As the research developing, it has been recognized that the research on associative memory of multi-valued patterns has very important practical value and theoretical significance.
Chaos control and associative memory of a time-delay globally coupled neural network using symmetric map
2011, NeurocomputingCitation Excerpt :In order to exhibit chaotic dynamics better, Aihara and his collaborators proposed chaotic neural network (CNN) models, which are composed of chaotic neurons derived based on the electrophysiological experiments on squid giant axons [1–6]. Ever since then a lot of chaotic neural network models [7–12] have been successively presented and investigated. There are several kinds of chaotic neural networks according to the mechanism of chaos.
Optimal matching by the transiently chaotic neural network
2009, Applied Soft Computing Journal
Xiangdong Liu was born in Hubei, China, in 1971. He received the Ph.D. degree in flight vehicle design from Harbin Institute of Technology, Heilongjiang, China, in 1998. His research interests include nonlinear dynamics of complex systems and chaos synchronization.
Chunbo Xiu was born in Heilongjiang, China, in 1978. He received the Ph.D. degree in Navigation, Guidance and Control from Beijing Institute of Technology, Beijing, China, in 2005. His research interests include neural networks, system modeling, and chaos control.