This paper introduces an evolutionary approach for training the adaptive network-based fuzzy inference system (ANFIS). The previous works are based on gradient descendent (GD); this algorithm converges very slowly and gets stuck down at bad local minima. This study applies one of the swarm intelligent branches, named particle swarm optimization (PSO), where the premise parameters of the rules are optimized by a PSO, and the conclusion part is optimized by least-squares estimation (LSE). The hybrid PSO-ANFIS model is performed for speaker recognition on CHAINS speech dataset. The results obtained by the hybrid model showed an improvement on the accuracy compared to similar ANFIS based on gradient descendent optimization.
Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single-and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.
This paper presents multi-modal biometric authentication approach using gait and electrocardiogram (ECG) signals, which can diminish the drawback of unimodal biometric approach as well as to improve authentication system performance. In acquisition phase, data sets are collected from three different databases, ECG-ID, MIT-BIH Arrhythmia database and UCI Machine Learning Repository (Gait). In Feature extraction phase of both signals (ECG and Gait) is performed by using 1D-local binary pattern. Features are obtained by merging two modalities as one feature. In classification approach, three classifiers are developed to classify subjects. K-nearest neighbour (KNN), relying on Euclidean distance, PNN (Probabilistic Neural Network), RBF (Radial Basis Function) and Support Vector Machine (SVM), relying on One-against-all (OAA). The proposed multimodal system has been tested over 18 subjects, and its identification accuracy was about 100%. Our result demonstrate that our approach outperforms rather than unimodal biometric system in terms of Correct Recognition Rate, Equal Error Rate, False Acceptance Rate and False Reject Rate.
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