Self-organizing Radial Basis Function Research Articles

Application of RBFNN to Optimal Self-Control

Application of an artificial neural network to optimal self-control of an autonomous system in a chaotic environment is described. The system is comprised of a network of sensors, a modeler, a controller, a plant and a utility estimator. The modeler contains a self-organizing radial basis function neural network and a conditional average estimator. A statistical model of influences from the environment, the system response and the utility is formed in the modeler during training. For this purpose the sensors provide signals representing the joint state of the environment and system while the utility estimator transforms these signals into utility descriptor. A vector comprised of the system and environment state variables, the control variable, and the utility is utilized in the self-organized adaptation of reference vectors. During adaptation the samples of control variable are generated randomly or by reinforcement procedure while during application the optimal control variable is estimated by a conditional average over memorized reference vectors. The control variable drives the plant and improves its performance. The method is demonstrated numerically by a self-stabilization of a chaotically kicked system. A perpetual information and control flow in the sensory-neural network of the self-controlled system is interpreted as a kind of machine consciousness.

Learning control using fuzzified self-organizing radial basis function network

This note describes an approach to integrating fuzzy reasoning systems with radial basis function (RBF) networks and shows how the integrated network can be employed as a multivariable self-organizing and self-learning fuzzy controller. In particular, by drawing some equivalence between a simplified fuzzy control algorithm (SFCA) and a RBF network, we conclude that the RBF network can be interpreted in the context of fuzzy systems and can be naturally fuzzified into a class of more general networks, referred to as FBFN, with a variety of basis functions (not necessarily globally radial) synthesized from each dimension by fuzzy logical operators. On the other hand, as a result of natural generalization from RBF to SFCA, we claim that the fuzzy system like RBF is capable of universal approximation. Next, the FBFN is used as a multivariable rule-based controller but with an assumption that no rule-base exists, leading to a challenging problem of how to construct such a rule-base directly from the control environment. We propose a simple and systematic approach to performing this task by using a fuzzified competitive self-organizing scheme and incorporating an iterative learning control algorithm into the system. We have applied the approach to a problem of multivariable blood pressure control with a FBFN-based controller having six inputs and two outputs, representing a complicated control structure. >