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POPFNN: a pseudo outer-product based fuzzy neural network
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A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
POP-Yager: A novel self-organizing fuzzy neural network based on the Yager inference
Expert Systems with Applications: An International Journal
POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improved MCMAC with momentum, neighborhood, and averagedtrapezoidal output
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern Recognition Letters
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
WSEAS Transactions on Information Science and Applications
Expert Systems with Applications: An International Journal
A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour
Expert Systems with Applications: An International Journal
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Hierarchical clustering for efficient memory allocation in CMAC neural network
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
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This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.