Changkyu Choi

Chaotic local search algorithm

The steepest descent search algorithm is modified in conjunction withchaos to solve the optimization problem of an unstructured search space. The problem is that given only the gradient information of the quality function at the present configuration,X(t), we must find the value of a configuration vector that minimizes the quality function. The proposed algorithm starts basically from the steepest descent search technique but at the prescribed points, i.e., local minimum points, the chaotic jump is performed by the dynamics of a chaotic neuron. Chaotic motions are mainly caused because the Gaussian function has a hysteresis as a refractoriness. An adaptation mechanism to adjust the size of the chaotic jump is also given. In order to enhance the probability of finding the global minimum, a parallel search strategy is developed. The validity of the proposed method is verified in simulation examples of the function minimization problem and the motion planning problem of a mobile robot.

Dynamical path-planning algorithm of a mobile robot: Local minima problem and nonstationary environments

A dynamical local path-planning algorithm of an autonomous mobile robot available for moving obstacle avoidance as well as stationary obstacle avoidance using artificial pressure and nonlinear friction is described. The dynamical path-planning algorithm is considered to adequately accommodate the mobile robot to a dynamic situation of a path-planning nature. Artificial pressure is just a conceptual idea and a mimicry of the real physical pressure. It can be thought of as a density gradient in the neighborhood of the mobile robot. Together with the previous virtual force field (VFF) method, the path of the mobile robot is a solution of a path-planning equation. Local minima problems in stationary environments are solved by introducing nonlinear friction into the chaotic neuron. Due to the nonlinear friction, the proposed path-planner reveals chaotic dynamics in some parameter regions. This new path-planner is feasible in guiding, on real-time, the mobile robot to avoid stationary obstacles and reach the goal. Computer simulations are presented to show the effectiveness of the proposed algorithm.

A modified genetic algorithm for neurocontrollers

Genetic algorithtiis are getting more popular nowadadvs because of their sirtiplicity and robustness. Genetic algorithm are global search techniques for optimizations and many other problems. A feed-forward neural network that is widely used in control applications usually learns by back propagation afgorithm(BP). However, when there exist certain constraints, BP cannot be applied. We apply a genetic algorithtti to such a case. To ittiprove hill-climbing capability and speed up the convergence, we propose a tnodified genetic algorifhnr(i2fGA). The validity and efficiency of the proposed algorithtti, hlG.4 are shown by various sitnulation examples of systetti identification and nonlinear svsteiti control such as cart-pole system and robot manipulators

Generalized asymmetrical bidirectional associative memory for multiple association

A classical bidirectional associative memory (BAM) suffers from low storage capacity and abundance of spurious memories though it has the properties of good generalization and noise immunity. In this paper, Hamming distance in recall procedure of usual asymmetrical BAM is replaced with modified Hamming distance by introducing weighting matrix into connection matrix. This generalization is validated to increase storage capacity, to lessen spurious memories, to enhance noise immunity, and to enable multiple association using simulation work.

Dynamical path-planning algorithm of a mobile robot using chaotic neuron model

This paper describes a dynamical local path-planning algorithm of an autonomous mobile robot available for stationary obstacle avoidance using nonlinear friction. Dynamical path-planning algorithm is considered to accommodate the mobile robot to the dynamic situation of the path-planning nature. Together with the previous virtual force field method, the path of the mobile robot is a solution of a path-planning equation. Local minima problems in stationary environments are solved by introducing nonlinear friction into the chaotic neuron. Because of the nonlinear friction, the proposed path-planner reveals chaotic dynamics in some parameter regions. This new path-planner is feasible to guide, in real-time, the mobile robot to avoid stationary obstacles and to reach the goal. Computer simulations are presented to show the effectiveness of the proposed algorithm.

New skill learning paradigm using various kinds of neurons

Modeled from human neurons, various types of artificial neurons are developed and applied to control algorithm. In this paper, the weights and structure of feedforward neural network controller are updated using new skill learning paradigm which consists of supervisory controller, chaotic neuron filter and associative memory. The pattern of system nonlinearity along the desired path is extracted while supervisory controller guarantees stability in the sense of the boundedness of tracking error. Next the pattern is divided into small segments and encoded to bipolar codes depending on the existence of critical points. Comparing the encoded pattern with pre-stored neural parameters and pattern pairs through associative memory, the most similar one is obtained. Also, chaotic neuron filter is used to add perturbation to neural parameters when the training of feedforward neural network is not successful with the pre-stored parameters. Finally the memory is updated with new successful parameters and pattern pairs. Simulation is performed for simple two-link robot in case of the slight modification of desired trajectory.

Information processing using chaos with application to mobile robot navigation problems

Throughout this study on information processing using an artificial neural network (ANN) and chaos we are attempting to devise a memory model that resembles human behavioral characteristics. For that purpose we construct a framework of the macroscopic model of the responding process in biological systems. Incoming stimuli are applied to the sensory receptors and preprocessed. A pattern-matching block allows one of the chaotic memories to find a feasible response in an associative way. After the chaotic memory is stabilized on one of the stable equilibrium points or limit cycles, its performance is evaluated. Since chaotic memory and the performance evaluation block form a feedback loop, they can handle features of the information blocks and store newly updated information blocks. Two kinds of chaotic memories are established in this paper: one is a 1-D map in which many information blocks can be stored as unstable periodic orbits, and the other is the famous Lozi attractor with rich dynamics. Simulations are performed for the mobile robot navigation problem in each case.