On reducing computational overhead in multi-objective genetic Takagi–Sugeno fuzzy systems

Analytical fuzzy approach to biological data analysis

The assessment of the physiological state of an individual requires an objective evaluation of biological data while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess. The analytical solution lends itself to a practical modeling algorithm facilitating the data classification. The experiments performed on the heartbeat interval data of 20 subjects verified that the proposed method is competing alternative to typically used pattern recognition and machine learning algorithms.

A Novel Approach to Construct Fuzzy Systems Based on Fireworks Optimization Algorithm

To find an optimal balance between accuracy and model transparency in fuzzy systems modeling, a novel approach based on the non-dominated sorting fireworks optimization algorithm (NSFOA) is proposed in this paper. The FOA is a new swarm intelligence algorithm mimicking the explosion of fireworks, which could be used to tune the parameters and optimize the structure of fuzzy systems with high effectiveness and correctness. At each iteration of the NSFOA algorithm, all individuals are sorted based on fast non-dominated sorting and crowding-distance evaluation, and the multiobjective fitness functions consider the aspects of both interpretability and accuracy contemporaneously. Subsequently, the individuals with better transparency and accuracy are selected for the next generation. The proposed NSFOA algorithm is applied to the second-order non-linear system and the Iris benchmark problem, and the experimental results demonstrate that our method leads to comprehensible fuzzy models with remarkable accuracy.