This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented.
Intelligent Soft-Computing Techniques in Robotics
24.1 24.2 Introduction Connectionist Approach in Robotics
Basic Concepts • Connectionist Models with Applications in Robotics • Learning Principles and Rules
Neural Network Issues in Robotics
Kinematic Robot Learning by Neural Networks • Dynamic Robot Learning at the Executive Control Level • Sensor-Based Robot Learning
24.4 Dustic M.
A hybrid neural fuzzy system is proposed to monitor both process mean and variance shifts simultaneously. One of the major components of the proposed system is composed of several feedforward neural networks that are trained off-line via simulation data. Fuzzy sets are also used to provide decision-making capability on uncertain neural network output. The hybrid control chart provides an alternative to