Eunjin Kim

Characterization of Interval Fuzzy Logic Systems of Connectives by Group Transformations

The global structure of various systems of logic connectives is investigated by looking at abstract group properties of the group of transformations of these. Such characterizations of fuzzy interval logics are examined in Sections 4–9. The paper starts by introducing readers to the Checklist Paradigm semantics of fuzzy interval logics (Sections 2 and 3). In the Appendix we present some basic notions of fuzzy logics, sets and many-valued logics in order to make the paper accessible to readers not familiar with fuzzy sets.

Object-shape recognition from tactile images using a feed-forward neural network

The sense of touch is an extremely important sensory system in the human body which helps to understand object shape, texture, hardness in the world around us. Incorporating artificial haptic sensory systems in rehabilitative aids and in various other human computer interfaces is a thrust area of research presently. This paper presents a novel approach of shape recognition and classification from the tactile pressure images by touching the surface of various real life objects. Here four objects (viz. a planar surface, object with one edge, a cuboid i.e. object with two edges and a cylindrical object) are used for shape recognition. The obtained tactile pressure images of the object surfaces are subjected to segmentation, edge detection and a mapping procedure to finally reconstruct the particular object shapes. The reconstructed images are used as features. The processed tactile pressure images are classified with feed- forward neural network (FFNN) using extracted features. The classifier performance is tested with different signal-to-noise (SNR) ratios. Is is observed that classifier accuracy decreases with decrease in SNR, but at SNR value 6 i.e. when the noise power is one sixth of the signal power, the mean classification accuracy of the classifier is 88%. This shows the robustness of feed-forward neural network in the classification purpose. The performance of FFNN is compared with four classifiers (Linear Discriminant Analysis, Linear Support vector machine, Radial Basis Function SVM, k-Nearest Neighbor). FFNN performed best acquiring first rank with a average classification accuracy of 94.0%.

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.

DE-TDQL: An adaptive memetic algorithm

Memetic algorithms are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolution. In this paper a synergism of the classical Differential Evolution algorithm and Q-learning is used to construct the memetic algorithm. Computer simulation with standard benchmark functions reveals that the proposed memetic algorithm outperforms three distinct Differential Evolution algorithms.

Reducing uncertainty in interval type-2 fuzzy sets for qualitative improvement in emotion recognition from facial expressions

The essence of the paper is to reduce uncertainty in interval type-2 fuzzy sets, and demonstrate the merit of uncertainty reduction in pattern classification problem. The area under the footprint of uncertainty has been used as the measure of uncertainty. A mathematical approach to reduce the area under the footprint of uncertainty has been proposed. Experiments have been designed to compare the relative performance of the classical interval type-2 fuzzy sets with its revised counterpart in emotion recognition from facial expression. Statistical tests performed favor the proposed results of uncertainty reduction. The proposed uncertainty reduction scheme helps in saving approximately 6% gain in classification accuracy with respect to one published work when applied to emotion recognition problem.

Characterization of Extended and Simplified Intelligent Water Drop (SIWD) Approaches and Their Comparison to the Intelligent Water Drop (IWD) Approach

This paper presents a simplified approach to performing the Intelligent Water Drops (IWD) process. This approach is designed to be comparatively lightweight while approximating the results of the full IWD process. The Simplified Intelligent Water Drops (SIWD) approach is specifically designed for applications where IWD must be run in a computationally limited environment (such as on a robot, UAV or small spacecraft) or where performance speed must be maximized for time sensitive applications. The SWID approach is described and compared and contracted to the base IWD approach.

Group transformations of systems of logic connectives

The global structure of systems of various logic connectives can be investigated by looking at abstract group properties of the group of their transformations. The paper examines such characterisations in some detail. It also look at such characterisation of some systems for interval fuzzy inference.

An Adaptive Memetic Algorithm using a synergy of Differential Evolution and Learning Automata

In recent years there has been a growing trend in the application of Memetic Algorithms for solving numerical optimization problems. They are population based search heuristics that integrate the benefits of natural and cultural evolution. In this paper, we propose an Adaptive Memetic Algorithm, named LA-DE which employs a competitive variant of Differential Evolution for global search and Learning Automata as the local search technique. During evolution Stochastic Automata Learning helps to balance the exploration and exploitation capabilities of DE resulting in local refinement. The proposed algorithm has been evaluated on a test-suite of 25 benchmark functions provided by CEC 2005 special session on real parameter optimization. Experimental results indicate that LA-DE outperforms several existing DE variants in terms of solution quality.

Non-Commutative Fuzzy Interval Logics with Approximation Semantics Based on the Checklist Paradigm and their Group Transformations

This paper continues investigation of systems of fuzzy interval logics based on the checklist paradigm semantics of Bandler and Kohout (1993). While the previous work was mainly concerned with the interval systems containing commutative AND and OR, this paper describes the system in which these connective types are non-commutative. While the commutative systems can be sufficiently characterized by an 8-element group of transformations, the non-commutative require the symmetric 16 element group S2times2times2times2

A distributed and decentralized approach for ant colony optimization with fuzzy parameter adaptation in traveling salesman problem

Ant Colony Optimization (ACO) is a swarm intelligence technique often applied to find solutions to hard optimization problems. In this paper, we present a new decentralized peer-to-peer approach for implementing ACO on distributed memory clusters. In addition, the approach is augmented with a fuzzy logic controller to reactively adapt several parameters of the ACO as a method of offsetting the increased exploitation resulting from the way in which information is shared between computing processes. We build an implementation of the approach for the Travelling Salesman Problem (TSP). The implementation is tested with several TSP problem instances with different numbers of processes in a cluster. The adaptive version is compared with the non-adaptive version and shown to agree with our expectations and performance is evaluated for different numbers of processes with an improvement shown.