An architecture for designing fuzzy logic controllers using neural networks

Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Fuzzy-neural control with application to a heating system

In this paper, a fuzzy-neuro approach will be presented for the design of bang-bang control system. A description of the architecture will be given. The multi-layered structure of the controller is essentially a neural network which resembles a fuzzy rule-based system. The robustness issue of an application to a heating system will also be described.published_or_final_versio

A generic architecture for hybrid intelligent systems

The integration of different learning and adaptation techniques in one architecture, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs. Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains. Due to the lack of a common framework it remains often difficult to compare the various systems conceptually and evaluate their performance comparatively. In this paper we first aim at classifying state-of-the-art intelligent systems, which have evolved over the past decade in the soft computing community. We identify four categories, based on the systems, overall architecture: (1) single component systems, (2) fusion-based systems, (3) hierarchical systems, and (4) hybrid systems. We then introduce a unifying paradigm, derived from concepts well known in the AI and agent community, as conceptual framework to better understand, modularize, compare and evaluate the individual approaches. We think it is crucial for the design of intelligent systems to focus on the integration and interaction of different learning techniques in one model rather then merging them to create ever new techniques. Two original instantiations of this framework are presented and discussed. Their performance is evaluated for prefetching of bulk data over wireless media