The Current Mode Fuzzy Logic Integrated Circuits Fabricated by the Standard CMOS Process
IEEE Transactions on Computers
Expert system technology: development and application
Expert system technology: development and application
Connectionist learning procedures
Artificial Intelligence
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Introduction to artificial neural systems
Introduction to artificial neural systems
Fuzzy regression analysis using neural networks
Fuzzy Sets and Systems
Backpropagation neural networks for fuzzy logic
Information Sciences: an International Journal
Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems
IEEE Transactions on Fuzzy Systems
Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment
Expert Systems with Applications: An International Journal
Incremental learning of dynamic fuzzy neural networks for accurate system modeling
Fuzzy Sets and Systems
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A neural fuzzy system learning with fuzzy training data is proposed in this study. The system is able to process and learn numerical information as well as word information. At first, we propose a basic structure of five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Also they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only word teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs. An experimental system is constructed to illustrate the performance and applicability of the proposed scheme.