Brain-computer interfacing is an emerging field of research where signals extracted from the human brain are used for decision making and generation of control signals. Selection of the right classifier to detect the mental states from electroencephalography (EEG) signal is an open area of research because of the signal's non-stationary and Ergodic nature. Though neural network based classifiers, like Adaptive Neural Fuzzy Inference System (ANFIS), act efficiently, to deal with the uncertainties involved in EEG signals, we have introduced interval type-2 fuzzy system in the fray to improve its uncertainty handling. Also, real-time scenarios require a classifier to detect more than two mental states. Thus, a multi-class discriminating algorithm based on the fusion of interval type-2 fuzzy logic and ANFIS, is introduced in this paper. Two variants of this algorithm have been developed on the basis of One-Vs-All and One-Vs-One methods. Both the variants have been tested on an experiment involving the real-time control of robot arm, where both the variants of the proposed classifier, produces an average success rate of reaching a target to 65% and 70% respectively. The result shows the competitiveness of our algorithm over other standard ones in the domain of non-stationary and uncertain signal data classification.
Abstract. Unlike static optimization problems, the position, height and width of the peaks may vary with time instances in dynamic optimization problems (DOPs). Many real world problems are dynamic in nature. Evolutionary Algorithms (EAs) have been considered to solve the DOPs in the recent years. This article proposes a multi-population based Differential Evolution algorithm which uses a local mutation to control the perturbation of individuals and also avoid premature convergence. An exclusion rule is used to maintain the diversity in a subpopulation to cover a larger search space. Speciation-based memory archive has been used to utilize the previously found optimal information in the new change instance. Furthermore the proposed algorithm has been compared with four other state-of-the-art EAs over the Moving Peak Benchmark (MPB) problem and a benchmarks set named Generalized Dynamic Benchmark Generator (GDBG) proposed for the 2009 IEEE Congress on Evolutionary Computation (CEC) competition.Keywords: Differential Evolution, local mutation, multi-population, dynamic optimization problems, speciation. IntroductionDifferential Evolution (DE) [1] is a very simple and popular algorithm for solving global optimization problems. It operates by means of computational steps which are similar to the EAs. However, unlike EAs, the members are perturbed by the scaled differences of the randomly taken and distinct vectors from the whole population. As it does not require any separate probability distribution, it is implicitly adaptive in this aspect. The popularity of DE is proliferating due to its simple structure, compactness, robustness and the parallel searching mechanism.Many real world problems are dynamic in nature. In case of DOPs, optimal solutions change with time. Hence we require algorithms that can detect the change in environment and should be able to track the optimum continuously [2]. A few dynamic real world problems are: price fluctuations, machine breakdown or maintenance, financial variations, stochastic arrival of new tasks etc. The main drawback of the conventional EAs under dynamic environment is loss of diversity,
This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.
This paper proposes a new classification algorithm which aims at predicting different states from an incoming non-stationary signal. To overcome the failure of standard classifiers at generalizing the patterns for such signals, we have proposed an Interval Type-2 Fuzzy based Adaptive neural fuzzy Inference System (ANFIS). Through the introduction IT2F system, we have aimed at improving the uncertainty management of the fuzzy inference system. Besides that using DE in forward and backward pass and improving the forward pass function we have improved the parameter update on wide range of nodal functions without any quadratic approximation in forward pass. The proposed algorithm is tested on a standard electroencephalography (EEG) dataset and it is noted that the proposed algorithm performs better than other standard classifiers including the classical ANFIS algorithm.
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