This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...

Real estate appraisal requires expert knowledge and should be performed by licensed professionals. Prior to the evaluation the appraiser must conduct a thorough study of the appraised property i.e. a land parcel and/or a building. Despite the fact that he sometimes uses the expertise of the surveyor, the builder, the economist or the mortgage lender, his estimations are usually subjective and a...

this paper proposes the exchange market algorithm (ema) to solve the combined economic and emission dispatch (ceed) problems in thermal power plants. the ema is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. existence of two seeking operators in ema provides a high ability in exploiting global optimum point. in order to show the capabilities ...

This paper presents a novel fuzzy identification method for dynamic modelling of quadrotor UAVs. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remai...

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using c...

This study investigates the evolutionary co-optimisation of fuzzy control and system parameters for the Resonating robot Arm (RA). The RA is a novel concept for a pick-and-place manipulator that uses a spring mechanism to reduce the required actuator torques. Since the performance of the total system depends on the combination of the spring mechanism and the controller it is difficult to find (...

This article presents a study on the use of parametrized operators in the Inference System of linguistic fuzzy systems adapted by evolutionary algorithms, for achieving better cooperation among fuzzy rules. This approach produces a kind of rule cooperation by means of the inference system, increasing the accuracy of the fuzzy system without losing its interpretability. We study the different al...

|This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process an evolutionary algorithm adjusts the membership functions or scaling factors of a prede ned fuzzy controller based on a performance index that speci es the desired control behavior. Genetic learning processes are concerned with the ...

This paper presents a new method for discovering the parameters of a fuzzy system, namely the combination of input variables of the rules, the parameters of the membership functions of the variables and a set of relevant rules, from numerical data using the newly proposed Bacterial Evolutionary Algorithm (BEA). In early work, the authors proposed the Pseudo-Bacterial Genetic Algorithm (PBGA) th...