Neural network design
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Ant Colony Optimization
Eliciting transparent fuzzy model using differential evolution
Applied Soft Computing
Statistical analysis of neural network modeling and identificationof nonlinear systems with memory
IEEE Transactions on Signal Processing
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A two-stage evolutionary process for designing TSK fuzzy rule-basedsystems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New membership functions for effective design and implementation of fuzzy systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning fuzzy rules with tabu search-an application to control
IEEE Transactions on Fuzzy Systems
Evolutionary design of fuzzy rule base for nonlinear system modeling and control
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
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Artificial life uses biological knowledge and techniques to solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. In this study, artificial life based approaches are handled and incorporated to enable a real-time water level control. The process was first modelled using NARX type Artificial Neural Network. A fuzzy controller was then attached to the model. For a better performance, fuzzy controller membership function boundary values and action values were optimized simultaneously. The optimization process was performed using genetic algorithm and ant colony optimization algorithm, respectively. Finally, the performance of the controllers was discussed further by considering the system outputs. The developed structure replaces the tedious process of trial-and-error for better combination of fuzzy parameters and can settle the problem of designing fuzzy controller without an expert's experience.