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Soft Computing
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Fundamentals of Robotics
This book starts with an introduction to robots and robotics. Forward and inverse kinematics problems of serial manipulators have been dealt in details. After discussing trajectory planning schemes, inverse dynamics problem of serial manipulator has been solved. A separate chapter has been devoted to the analysis of wheeled robot. It then concentrates on analysis of two-legged robot. The working principles of different types of sensors used in robots have been explained in one chapter. Various steps involved in robot vision have then been discussed in detail. The last chapter deals with different motion planning schemes of robots. It has been written to fulfill the requirements of a large number of readers belonging to various disciplines of engineering. It will be very much helpful to the students, scientists and practicing engineers.
Soft Computing
Soft Computing starts with an introduction to soft computing, a family consists of many members, namely genetic algorithms (GAs), fuzzy logic (FL), neural networks (NNs), and others. To realize the need for a non-traditional optimization tool like GA, one chapter is devoted to explain the principle of traditional optimization. The working cycle of a GA is explained in detail. The mechanisms of some specialized GAs are then discussed with some appropriate examples. The working principles of some other non-traditional optimization tools like simulated annealing (SA) and particle swarm optimization (PSO) are discussed in detail. Multi-objective optimization has been dealt in a separate chapter,...
Artificial intelligence and Soft computing
This book is prepared for the engineering students pursuing degree in computer science and information technology branch. The main consideration in writing the book is to present the considerable requirements of the syllabus in a simple manner as possible. This book contains many solved examples which will help student to gain confidence in problem solving. Valuable suggestion is heartily welcome for further improvement of this book
Intelligent Autonomous Systems
Intelligent Autonomous Systems (IAS) are the physical embodiment of machine intelligence providing a core concept for integrating various advanced techno- gies with pattern recognition and learning. The basic philosophy of IAS research is to explore and understand the nature of intelligence in problems of perception, reasoning, learning and control in order to develop and implement the theory to engineered realization. In other words, the objective is to formulate various me- odologies for the development of robots which can operate autonomously and exhibit intelligent behavior by making appropriate decisions to perform the right task at the right time. Since IAS basically deals with the int...
Data Mining in Biomedical Imaging, Signaling, and Systems
Data mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedi
Soft Computing
SOFT COMPUTING: Fundamentals and Applications starts with an introduction to soft computing, a family consists of many members, namely Genetic Algorithms (GAs), Fuzzy Logic (FL), Neural Networks (NNs), and others. To realize the need for a non-traditional optimization tool like GA, one chapter is devoted to explain the principle of traditional optimization. The working cycle of a GA is explained in detail. The mechanisms of some specialized GAs are then discussed with some appropriate examples. The working principles of some other non-traditional optimization tools like Simulated Annealing (SA) and Particle Swarm Optimization (PSO) are discussed in detail. Multi-objective optimization has be...
Metaheuristics for Dynamic Optimization
This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools...
Simulated Evolution and Learning
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