Amit Konar

Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets

The paper introduces an alternative approach to time-series prediction for stock index data using Interval Type-2 Fuzzy Sets. The work differs from the existing research on time-series prediction by the following counts. First, partitions of the time-series, obtained by fragmenting its valuation space over disjoint equal sized intervals, are represented by Interval Type-2 Fuzzy Sets (or Type-1 fuzzy sets in absence of sufficient data points in the partitions). Second, an Interval Type-2 (or type-1) fuzzy reasoning is performed using prediction rules, extracted from the (main factor) time-series. Third, a type-2 (or type-1) centroidal defuzzification is undertaken to determine crisp measure of inferences obtained from the fired rules, and lastly a weighted averaging of the defuzzified outcomes of the fired rules is performed to predict the time-series at the next time point from its current value. Besides the above three main prediction steps, the other issues considered in the paper include: (i) employing a new strategy to induce the main factor time-series prediction by its secondary factors (other reference time-series) and (ii) self-adaptation of membership functions to properly tune them to capture the sudden changes in the main-factor time-series. Performance analysis undertaken reveals that the proposed prediction algorithm outperforms existing algorithms with respect to root mean-square error by a large margin (?23%). A statistical analysis undertaken with paired t-test confirms that the proposed method is superior in performance at 95% confidence level to most of the existing techniques with root mean square error as the key metric.

Adaptive Firefly Algorithm for nonholonomic motion planning of car-like system

This paper provides a novel approach to design an Adaptive Firefly Algorithm using self-adaptation of the algorithm control parameter values by learning from their previous experiences in generating quality solutions. Computer simulations undertaken on a well-known set of 25 benchmark functions reveals that incorporation of Q-learning in Firefly Algorithm makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed adaptive firefly algorithm has been studied on an automatic motion planing problem of nonholonomic car-like system. Experimental results obtained indicate that the proposed algorithm based parking scheme outperforms classical Firefly Algorithm and Particle Swarm Optimization with respect to two standard metrics defined in the literature.

A modified Imperialist Competitive Algorithm for multi-robot stick-carrying application

The paper proposes a novel evolutionary optimization approach of solving a multi-robot stick-carrying problem. The problem refers to determine the time-optimal trajectory of a stick, being carried by two robots, from a given starting position to a predefined goal position amidst static obstacles in a robot world-map. The problem has been solved using a new hybrid evolutionary algorithm. Hybridization, in the context of evolutionary optimization framework, refers to developing new algorithms by synergistically combining the composite benefits of global exploration and local exploitation capabilities of different ancestor algorithms. The paper proposes a novel approach to embed the motion dynamics of fireflies of the Firefly Algorithm (FA) into a socio-political evolution-based meta-heuristic search algorithm, known as the Imperialist Competitive Algorithm (ICA). The proposed algorithm also uses a modified random-walk strategy based on the position of the candidate solutions in the search space to effectually balance the trade-off between exploration and exploitation. Thirteen other state-of-art techniques have been used here to study the relative performance of the proposed Imperialist Competitive Firefly Algorithm (ICFA) with respect to run-time and accuracy (offset in objective function from the theoretical optimum after termination of the algorithm). Computer simulations undertaken on a well-known set of 25 benchmark functions reveal that the incorporation of the proposed strategies into the traditional ICA makes it more efficient in both run-time and accuracy. The performance of the proposed algorithm has then finally been studied on the real-time multi-robot stick-carrying problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm based stick-carrying scheme outperforms other state-of-art techniques with respect to two standard metrics defined in the literature. The application justifies the importance of the proposed hybridization and parameter adaptation strategies in practical systems. Multi-robot stick-carrying problem is solved by the proposed ICFA.ICFA is fusion of motion dynamics of Firefly and Imperialist Competitive Algorithm.Modified random-walk strategy is proposed to balance exploration/exploitation.Simulation results confirm efficiency of the proposed ICFA in the state-of-art.Experiment with twin Khepera-II mobile robots is done amidst static obstacles.

Call Admission Control in Mobile Cellular Networks

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Introduction to Emotional Intelligence

This chapter provides an introduction to emotional intelligence. It attempts to define emotion from different perspectives, and explores possible causes and varieties. Typical characteristics of emotion, such as great intensity, instability, partial perspectives and brevity are outlined next. The evolution of emotion arousal through four primitive phases such as cognition, evaluation, motivation and feeling is briefly introduced. The latter part of the chapter emphasizes the relationship between emotion and rational reasoning. The biological basis of emotion and the cognitive model of its self-regulation are discussed at the end of the chapter.

Improving the speed of convergence of multi-agent Q-learning for cooperative task-planning by a robot-team

Abstract Learning-based planning algorithms are currently gaining popularity for their increasing applications in real-time planning and cooperation of robots. The paper aims at extending traditional multi-agent Q-learning algorithms to improve their speed of convergence by incorporating two interesting properties, concerning (i) exploration of the team-goal and (ii) selection of joint action at a given joint state. The exploration of team-goal is realized by allowing the agents, capable of reaching their goals, to wait at their individual goal states, until remaining agents explore their individual goals synchronously or asynchronously. To avoid unwanted never-ending wait-loops, an upper bound to wait-interval, obtained empirically for the waiting team members, is introduced. Selection of joint action, which is a crucial problem in traditional multi-agent Q-learning, is performed here by taking the intersection of individual preferred joint actions of all the agents. In case the resulting intersection is a null set, the individual actions are selected randomly or otherwise following classical multi-agent Q-learning. It is shown both theoretically and experimentally that the extended algorithms outperform its traditional counterpart with respect to speed of convergence. To ensure selection of right joint action at each step of planning, we offer high rewards to exploration of the team-goal and zero rewards to exploration of individual goals during the learning phase. The introduction of the above strategy results in an enriched joint Q-table, the consultation of which during the multi-agent planning yields significant improvement in the performance of cooperative planning of robots. Hardwired realization of the proposed learning based planning algorithm, designed for object-transportation application, confirms the relative merits of the proposed technique over contestant algorithms.

Differential evolution with temporal difference Q-learning based feature selection for motor imagery EEG data

Electroencephalograph (EEG) based Braincomputer Interface (BCI) research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a dataset to improve the accuracy of classification. For this, we have employed Differential Evolution with Temporal Difference Q-Learning (DE-TDQL)-based clustering algorithm to reduce the features and have acquired their corresponding accuracy. Experiments with synthetic and real-world data provide evidence that such an approach leads to improved classification performance. Superiority of the new method is demonstrated by comparing it with three classification methods including Linear Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine-Radial Basis Function. Self-Adaptive Differential Evolution, Differential Evolution/current-to-best/l, Particle Swarm Optimization and Genetic Algorithm-based clustering approaches have also been used here to study the relative performance of the proposed adaptive memetic algorithm-based clustering technique with respect to runtime and classification accuracy.

Fuzzy Models for Facial Expression-Based Emotion Recognition and Control

The chapter examines the scope of fuzzy relational approach to human emotion recognition from facial expressions, and its control. Commercial audio-visual movies pre-selected for exciting specific emotions have been presented before subjects to arouse their emotions. The video clips of their facial expressions describing the emotions are recorded and analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye-opening, mouth-opening and the length of eyebrow-constriction are next extracted from the localized regions. These features are then fuzzified, and mapped on to an emotion space by employing Mamdani type relational model. A scheme for the validation of the system parameters is also presented. The later part of the chapter provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state using suitable audio-visual movies. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust with a good level of experimental accuracy.

Propositional syntax and semantics induced knowledge re-structuring in a fuzzy logic network for ad hoc reasoning

Traditional approaches to fuzzy reasoning usually employ Generalized Modus Ponens, Generalized Modus Tollens and Generalized Hypothetical Syllogisms to derive fuzzy inferences from a given set of fuzzy observations/facts and fuzzy if-then rules. However, the above approaches occasionally fail to generate inferences, although semantically plausible fuzzy inferences follow as a natural consequence of the given set of rules and fuzzy facts/observations. This paper serves to fill this void. It extends propositional syntax and semantics in the context of fuzzy reasoning to transform/re-structure individual rules to a suitable form (by negating and transposing propositions from antecedent to consequent and vice-versa), so as to get the propositions present in the antecedent (consequent) instantiated (refuted) by positive (negative) facts to enable the rules for forward (backward) firing. Next fuzzy compositional rule of inference is used to perform forward/backward reasoning. Consequently, when the rules are embedded in a fuzzy Petri net like structure, the rules apparently fire randomly in forward or backward direction without maintaining any topological order in transition-firing. Such random firing of rules in distributed parts of the network is referred to as ad hoc reasoning. Here, we transform such randomly ordered forward/backward firing sequence of transitions in the network into a fixed topological order of transition-firing in forward direction only by replacing backward firing transitions into equivalent forward firing transitions. The proposed method of automated reasoning has successfully been applied in diagnostic application of an electronic rectifier circuit, and the results are appealing. Re-structuring of fuzzy production rules.Automated Forward/Backward chaining.Diagnosis.

Fuzzy logic and differential evolution‐based hybrid system for gesture recognition using Kinect sensor

Expert Systems. 2017;e12210. https://doi.org/10.1111/exsy.12210 Abstract The paper introduces a novel approach to gesture recognition aimed at physical disorder identification capable of handling variations in disorder expressions. The gestures are captured by Microsofts Kinect sensor. The work is segmented into four main parts. The first stage describes a “relax” posture through four centroids depicting four portions of the skeletal structure. In the second stage, when the subject is showing symptoms of any one of the 16 physical disorders, then the skeletal structure distorts; the bilateral structure is lost, and concept of “centroid” computation does not seem relevant. Hence, in the second stage, “motion points” depicting shifted centroids for the distorted posture are computed by distance maximization with respect to the four corresponding centroids obtained for the relax posture. This process is carried out by adapting the weights assigned to each joint by differential evolution. In the third stage, eight features are figured out on the basis of Euclidean distances and angles among the motion points of the distorted gesture. In the final stage, gestures are recognized using an interval type‐2 fuzzy set‐based classifier with 91.37% accuracy.