Guoguang He

Controlling chaos in a chaotic neural network

The chaotic neural network constructed with chaotic neuron shows the associative memory function, but its memory searching process cannot be stabilized in a stored state because of the chaotic motion of the network. In this paper, a pinning control method focused on the chaotic neural network is proposed. The computer simulation proves that the chaos in the chaotic neural network can be controlled with this method and the states of the network can converge in one of its stored patterns if the control strength and the pinning density are chosen suitable. It is found that in general the threshold of the control strength of a controlled network is smaller at higher pinned density and the chaos of the chaotic neural network can be controlled more easily if the pinning control is added to the variant neurons between the initial pattern and the target pattern.

2008 Special Issue: Threshold control of chaotic neural network

The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it is necessary to control chaos in the chaotic neural network. We have studied the chaotic neural network with threshold activated coupling, which provides a controlled network with associative memory dynamics. The network converges to one of its stored patterns or/and reverse patterns which has the smallest Hamming distance from the initial state of the network. The range of the threshold applied to control the neurons in the network depends on the noise level in the initial pattern and decreases with the increase of noise. The chaos control in the chaotic neural network by threshold activated coupling at varying time interval provides controlled output patterns with different temporal periods which depend upon the control parameters.

Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identification, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work clarifies the application prospect of the associative dynamics of the chaotic neural network.

Controlling a chaotic neural network for information processing

A dynamic phase-space constraint method is proposed to control complex chaotic dynamics in a chaotic neural network (CNN), by limiting refractoriness internal states with a time-varying threshold. The limiting threshold evolves according to a control signal derived from the feedback internal states of the network. Simulation results reveal that the CNN under control exhibits multiphase behavior in the control parameter space. With proper parameter values, the controlled CNN converges to a periodic orbit which includes a stored pattern that has the smallest Hamming distance to its initial state. The properties of the controlled CNN can be used for information processing such as memory retrieval and pattern recognition.

Associative Dynamics and Its Control of Chaotic Neural Network

In this paper, the Nagumo-Sato model is used to construct a chaotic neural network (CNN). Each reference sample is stored in the chaos attractor that is formed by the associative dynamics. When the inputs sometimes deviate obviously from its original attractive region and the correct association is not realized by itself, the feedback pinning is use to control the associative dynamics. The lost original memory will be retrieved quickly considering the fact that the CNN is a spatiotemporal system. The simulation experiments of both the associative dynamics and the retrieval process are done for the faults of broken rotor bars of an induction motor. The results show that the feedback pinning control is a simple and effective control method to the CNN.

Elimination of spiral waves in a locally connected chaotic neural network by a dynamic phase space constraint

In this study, a method is proposed that eliminates spiral waves in a locally connected chaotic neural network (CNN) under some simplified conditions, using a dynamic phase space constraint (DPSC) as a control method. In this method, a control signal is constructed from the feedback internal states of the neurons to detect phase singularities based on their amplitude reduction, before modulating a threshold value to truncate the refractory internal states of the neurons and terminate the spirals. Simulations showed that with appropriate parameter settings, the network was directed from a spiral wave state into either a plane wave (PW) state or a synchronized oscillation (SO) state, where the control vanished automatically and left the original CNN model unaltered. Each type of state had a characteristic oscillation frequency, where spiral wave states had the highest, and the intra-control dynamics was dominated by low-frequency components, thereby indicating slow adjustments to the state variables. In addition, the PW-inducing and SO-inducing control processes were distinct, where the former generally had longer durations but smaller average proportions of affected neurons in the network. Furthermore, variations in the control parameter allowed partial selectivity of the control results, which were accompanied by modulation of the control processes. The results of this study broaden the applicability of DPSC to chaos control and they may also facilitate the utilization of locally connected CNNs in memory retrieval and the exploration of traveling wave dynamics in biological neural networks.

Multi-frequency sinusoidal wave control in a chaotic neural network

Brain waves are classified as gamma, beta, alpha, theta, and delta waves to quantify brain activity and can be approximated as sinusoidal waves of different frequencies. In this work, we use sinusoidal waves at two different frequencies to control chaos in a chaotic neural network (CNN) to explore the effect of multi-frequency sinusoidal waves in chaos control. We propose two methods to control chaos. In one, two sinusoidal wave signals are added to different groups of neurons. In the other, a control signal with a mixture of two sinusoidal waves with different frequencies is added to all neurons. The controlling dynamics differ in these two cases. A stable output sequence of the controlled CNN contains only one type of stored pattern and its reversed pattern, which are related to the initial pattern.

An Improved Delay Feedback Control Method in a Chaotic Neural Network

In this paper, we proposed an improved delay feedback control method (IDFC) for a chaotic neural network. In the method, a delay feedback control signal is added into the term of the refractoriness of the chaotic neuron to resist the chaos in the chaotic neural network. The computer experiments show that the output sequence of the controlled chaotic neural network become periodic. The controlling chaos in a chaotic neural network is therefore implemented. When control parameters K and tau are taken as 1.3 and 1 respectively, the outputs of the network is periodic-16

Design of a cryogenic giant magnetostrictive actuator using HTS

Publisher Summary This chapter explores a cryogenic giant magnetostrictive materials (CGMM) actuator using HTS, taking into account both the coupled field characteristics of the CGMM and the anisotropy of the investigated Bi2223/Ag HTS tapes. The radial component of the magnetic field on the HTS magnet is reduced greatly by a magnetic circuit using laminated silicon iron. So the parallel component becomes an important factor limiting the critical current too owing to much larger magnitude, which is more than 10 times the radial component. Since both the two components have to be considered, a HTS solenoid of simple cylindrical configuration is designed, which is used to provide the CGMM a magnetic field of good uniformity making it to the state of saturation. Valid coupled field iteration of finite element method (FEM) for the coupled field calculation of the smart material like the GMM have been developed. The genetic arithmetic concerning HTS anisotropy characteristic combined with the coupled field iteration of FEM is used for optimization. The analysis result shows CGMM can be saturated with field of good uniformity when the HTS has the parameters of aa=0.00339m, height=0.01594m, length=0.00920m, which cost the least HTS tapes. It is also found that the maximum current density is permissive 3700A/cm 2 with these optimized parameters.

Novel cryogenic motion control for aerospace

Publisher Summary This chapter exhibits two types of novel cryogenic mechanisms, which has been designed using high-temperature superconducting (HTS) and magnetostrictive materials. Magnetostrictive materials have lead to a variety of large-stroke, high-force actuators at cryogenic temperature, which was previously difficult to make and is now much easier. For operation at higher than 10K and below 77K, the HTS offers an attractive opportunity to incorporate the HTS into mechanisms while taking advantage of their persistent mode of operation. Novel constructs and motion control capabilities of two types of the rare earth magnetostrictive devices incorporated with HTS, an actuator and a linear stepping motor, are exhibited at the cryogenic condition. Using HTS tapes in magnet takes advantage of the cryogenic temperature environment for added motive efficiency. Innovative actuator or motor designs using HTS magnet with the application of specific drive electronics bring out efficient, compact, and lightweight actuator systems for aerospace. The potential exists for the quick advancement of related techniques for producing actuators and mechanisms at the cryogenic temperature environment.