Kenya Jin'no

A relationship between network topology and search performance of PSO

Particle swarm optimization (abbr. PSO) is one of the most effective optimization algorithms. The PSO contains many control parameters, therefore, the performance of the searching ability of the PSO is significantly alternated. In order to analyze the dynamics of such PSO system rigorously, we have analyzed a deterministic PSO (abbr. D-PSO) systems which does not contain any stochastic factors, and its coordinate of the phase space is normalized. The found global best information influences the dynamics. This situation can be regarded as the full-connection state. On the other hand, there is the case where the best information in a limited population. Such information is called as lbest. How to get the lbest information from any population is equivalent to a network structure. Such network structure influences the performance of searching ability. In order to clarify a relationship between network structures of the PSO and its performance, we pay attention to the degree and the average distance used in graph theory. We consider the two cases where the D-PSO has an extended cycle structure and a Small World network structure. Our numerical simulation results indicates the searching performance of the D-PSO is depended on the average distance of the node. Especially, the long average distance exerts the search performance on the D-PSO. We confirm that the search performance properties of the D-PSO and the conventional stochastic PSO are completely different to the average distance. The search performance of the D-PSO is improved according to the average distance. On the other hand, the search performance of the conventional stochastic PSO is deteriorated according to the average distance. We consider that the slow transmission of the beneficial information leads to the diversification of the particles of the D-PSO. Also, we clarify the small perturbation of the random range of the stochastic PSO is important.

Analysis of bifurcation phenomena in a 3-cells hysteresis neural network

This paper considers bifurcation phenomena in a simplified hysteresis neural network. The network consists of three cells and has three control parameters. We have discovered that the simple system exhibits various attractors: stable equilibria, periodic orbits, and chaos. Since the system is piecewise linear, the return map and Lyapunov exponents are calculated by using the piecewise exact solution. Using the mapping procedure, the bifurcation mechanism of stable equilibria and three kinds of bifurcation mechanisms of periodic orbits have been clarified. In addition, chaos has been analyzed by using Lyapunov exponents of the return map.

Analysis and synthesis of a continuous-time hysteresis neural network

A piecewise-linear hysteresis associative memory is discussed. Conditions on parameters for guaranteed storage of all desired memories, global convergence of an energy function, and control of stable spurious output are given. The networks are then synthesized using a mixed autocorrelation/pseudoinverse matrix. In some range of their mixed rate, it is numerically confirmed that the performance is improved vastly. A remarkable hysteresis effect in the global convergence properties of each desired memory is found.<<ETX>>

Analysis of dynamical characteristic of canonical deterministic PSO

A particle swarm optimization (PSO) system is one of the powerful systems for solving global optimization problems. The PSO algorithm can search an optimal value of a given evaluation function quickly compared with other proposed meta-heuristics algorithms. The conventional PSO system contains some random factors, therefore, the dynamics of the system can be regarded as stochastic dynamics. In order to analyze the dynamics rigorously, some papers pay attention to deterministic PSO systems which does not contain any stochastic factors. According to these results, the eigenvalues of the system impinge on the dynamics of the particles. Depending on the parameter, the searching ability of the deterministic PSO is decreased. Also, the eigenvalue is complex conjugate number, the system exhibits remarkable searching ability. In order to overcome this, we propose a canonical deterministic PSO which can control its eigenvalues easily, and can improve the searching ability. The dynamics of the system can characterize the damping factor and the rotation angle which can derive from its eigenvalue. We will confirm relation between these parameters and the searching ability of the optimal value from some numerical simulations.

PSO-based multiple optima search systems with switched topology

This paper discusses a particle swarm optimization (PSO) with switched topology and its application to the multi-solution problems. First, we introduce a deterministic PSO characterized by normalized deterministic parameters and a canonical form system equation. This system is convenient to grasp effects of parameters on the stability. Second, we investigate effects of the average distance of several the swarm topologies on the search capability. Especially, we introduce the switched topology where any information is not transmitted from the edge if the switch is off. Third, we consider an application to exploring multiple periodic points in simple dynamical systems. Performing numerical experiments for typical examples, the algorithm performance is investigated.

Chaotic artificial neural system and its control

This paper discusses control of chaos from an artificial neural cell. The cell is piecewise linear and includes bipolar hysteresis. A periodic stimulation switches the action potential represented by oscillation frequency and causes chaotic response. Using a mapping procedure, we can prove chaos generation and calculate the initial point of unstable periodic orbits (UPO) embedded in chaos. Then we can stabilize the desired UPO by using a modified occasional proportional feedback (OPF) which is applied only if the state is in a linear region and must stabilize the desired UPO in a theoretical sense. This OPF function is verified by laboratory experiments.

Particle swarm optimization for single phase PWM inverters

This article discusses a design procedure of DC-AC inverter. The DC-AC inverter is to produce a sinusoidal AC voltage with adjustable amplitude and frequency. Pulse-width modulation (abbr. PWM) is one of the most used techniques in static inverters. For the PWM, the switching angle is most important, and the switching angle controls the efficiency of DC-AC inversion. For this reason, the design of the optimal switching angle vector is very important. In this article. we obtain such switching angle vector by particle swarm optimization system (abbr. PSO). Our simulation results indicate that the proposed design procedure gives high efficiency inversion.

Analysis of convergence property of PSO and its application to nonlinear blind source separation

This article analyzes the convergence property of the particle swarm optimization and its application to the nonlinear blind source separation system. The inter-particle communication of the particle swarm optimization is realized by the past history of the neighbors and depends on the network structure of the swarm. We focus on an average path length of the network, and we clarify the relationship between the average path length and its searching performance. The result indicates that a long average path length is effective for multi-modal functions and multi-optima problems. Therefore, we apply the PSO with the long average path length to a nonlinear blind source separation system. Blind source separation is a technique for recovering an original source signal from mixing signals without the aid of information of the source signal. The system restores the original signal using the probability of the distribution of the original signal. In this study, we consider the case where the original signals are nonlinearly mixed. In general, the separation of the nonlinear mixture signals is quite difficult. In order to solve such problem, we apply a radial basis function network to the nonlinear blind source separation system. The radial basis function network can approximate the nonlinear mapping. Therefore, the inverse mapping of nonlinear mixture system is approximated by the RBF network. For the system to be able to approximate the inverse mapping, it is necessary to learn the parameter of the RBF network. PSO is used for a learning algorithm. Simulation results show that the proposed approach has good performance.

Queuing Scheme for Improved Downlink Throughput on WLANs

A transmission queuing scheme is described that increases downlink throughput on wireless local area networks (WLANs) while also increasing the total throughput. When the amount of uplink traffic increases on a WLAN, the carrier sense multiple access with collision avoidance (CSMA/CA) protocol, which is the prescribed scheme for IEEE 802.11 WLAN channel access, may substantially reduce the rate of downlink data frame transmission. This results in severe throughput degradation for mobile stations with downlink traffic. The proposed scheme comprises a transmission control function based on consecutive transmission, as described in the IEEE 802.11e standard, and a dynamic queue prioritization algorithm. Simulation results demonstrate that the proposed scheme increases the maximum total throughput for uplink and downlink traffic by 17% compared with the conventional distributed coordination function (DCF) scheme and that it reduces the difference between uplink and downlink throughput. In an environment where transmission errors occur, the difference in throughput is reduced by about 50% compared with the conventional schemes.

Particle swarm optimization with switched topology and deterministic parameters

This paper studies the particle swarm optimization (PSO) with switched topology and its application to the multi-solution problems (MSPs). The switched topology can give variety in the swarm structure and can be effective to control information transmission speed. We introduce the switched distance as a basic measure to evaluate the switched topology. The proposed PSO does not include stochastic parameters and is suitable for theoretical analysis. Applying the proposed PSO to typical benchmarks of the MSPs, the algorithm efficiency is investigated.