A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

Neural-network-based fuzzy logic control and decision system

Foundations of Fuzzy Control , Foundations of Fuzzy Control , کتابخانه الکترونیک و دیجیتال - آذرسا

Foundations of fuzzy control

A new methodology is proposed for designing a fuzzy logic controller (FLC). A phase plane is used to bridge the gap between the time-response and rule base. The rule base can be easily built using the general dynamics of the process, and then readily updated to contain the delayed information for reducing the deadtime effects of the process. An adaptive gain method is also proposed to help the database design and the controller tuning. Much of the FLC design can be shifted to the design and tuning of gain. A good performance can be achieved both in transient state and steady state without use of multidecision tables. Application of FLC with these new methodologies is presented for a thermal process with a varying deadtime to show the robust performance of FLC and the effectiveness of these methodologies. >

A new methodology for designing a fuzzy logic controller

The paper presents a classification and analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Fuzzy logic is shown to be a very promising mathematical approach to modeling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation engineering problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Possibilities are shown regarding the further application of fuzzy logic in this field.

Fuzzy logic systems for transportation engineering: the state of the art

A fuzzy-logic based multi-input/multi-output roll controller designed for the Advanced Technology Wing (ATW) aircraft model is presented. The ATW integrates active controls with a flexible wing structure to provide optimal wing shapes to meet particular flight performance criteria. The use of a fuzzy controller for roll rate and load alleviation control was investigated. Fuzzy rules were developed to determine the appropriate control surface deflections to achieve the desired roll rate while ensuring that wing loads are within safe bounds. The modulation of the damping factor is according to the distance of the system state from the goal state. This damping modulation technique allows full utilization of the vehicle's acceleration capability and resulted in an improvement of the response time by a factor of two. The resultant fuzzy controller commands six surface deflections to control the roll rate and four torsion moments. >

Fuzzy logic for control of roll and moment for a flexible wing aircraft

Fuzzy logic is examined, and its application to control systems is discussed. The steps taken to design a fuzzy controller are described, and the possibility of interfacing fuzzy logic to existing control systems is noted. Tools for developing and modeling fuzzy control systems are described. >

K. Self

Papers

Designing with fuzzy logic