Soft Computing VS Hard Computing

"Soft computing" is just automating process of computation. "Hard computing" means you are just doing computation process according to your needs only. For eg. there are kind of problems[identifying vowels and counting them in any given sentence] which a human brain can compute very easily. This is an example of "Soft Computing". Our human brain scans vowels and counts them in seconds. Now coming to "Hard computing" - you make a program for this same above scenario and run it. That is part of "Hard Computing". You are telling the computer -just a box to compute(or process) according to your needs. So that kind of computing is a part of "Hard computing". "Soft computing" is much much times faster than "Hard Computing". Basic constituents of "Soft Computing" include Fuzzy Logic , Neural computing, Evolutionary computation, Machine learning and probabilistic reasong etc.

Hard computing can be made analogy to "Hard Coding" and Soft Computing to "Soft coding"

Introduction to soft Computing

Soft computing (SC) is a branch, in which, it is tried to build intelligent and wiser machines. Intelligence provides the power to derive the answer and not simply arrive to the answer. Purity of thinking, machine intelligence, freedom to work, dimensions, complexity and fuzziness handling capability increase, as we go higher and higher in the hierarchy as shown in Fig. 1.1. The final aim is to develop a computer or a machine which will work in a similar way as human beings can do, i.e. the wisdom of human beings can be replicated in computers in some artificial manner.

Intuitive consciousness/wisdom is also one of the important area in the soft computing, which is always cultivated by meditation. This is indeed, an extraordinary challenge and virtually a new phenomenon, to include consciousness into the computers.

Soft computing is an emerging collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability and total low cost. Soft computing methodologies have been advantageous in many applications. In contrast to analytical methods, soft computing methodologies mimic consciousness and cognition in several important respects: they can learn from experience; they can universalize into domains where direct experience is absent; and, through parallel computer architectures that simulate biological processes, they can perform mapping from inputs to the outputs faster than inherently serial analytical representations. The trade off, however, is a decrease in accuracy. If a tendency towards imprecision could be tolerated, then it should be possible to extend the scope of the applications even to those problems where the analytical and mathematical representations are readily available. The motivation for such an extension is the expected decrease in computational load and consequent increase of computation speeds that permit more robust system (Jang et al. 1997).

Syllabus of Soft Computing

RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA, BHOPAL

PROGRAMME: B.E.

Computer Science & Engineering, VIII semester

Soft Computing 

CS801

Unit – I

Soft Computing : Introduction of soft computing, soft computing vs. hard computing,

various types of soft computing techniques, applications of soft computing.

Artificial Intelligence : Introduction, Various types of production systems, characteristics of

production systems, breadth first search, depth first search techniques, other Search Techniques

like hill Climbing, Best first Search, A* algorithm, AO* Algorithms and various types of control

strategies. Knowledge representation issues, Prepositional and predicate logic, monotonic and

non monotonic reasoning, forward Reasoning, backward reasoning, Weak & Strong Slot & filler

structures, NLP.

Unit – II

Neural Network : Structure and Function of a single neuron: Biological neuron, artificial

neuron, definition of ANN, Taxonomy of neural net, Difference between ANN and human brain,

characteristics and applications of ANN, single layer network, Perceptron training algorithm,

Linear separability, Widrow & Hebb;s learning rule/Delta rule, ADALINE, MADALINE, AI v/s ANN.

Introduction of MLP, different activation functions, Error back propagation algorithm, derivation of

BBPA, momentum, limitation, characteristics and application of EBPA,

Unit – III

Counter propagation network, architecture, functioning & characteristics of counter

Propagation network, Hopfield/ Recurrent network, configuration, stability constraints, associative

memory, and characteristics, limitations and applications. Hopfield v/s Boltzman machine.

Adaptive Resonance Theory: Architecture, classifications, Implementation and training.

Associative Memory.

Unit – IV

Fuzzy Logic: Fuzzy set theory, Fuzzy set versus crisp set, Crisp relation & fuzzy relations,

Fuzzy systems: crisp logic, fuzzy logic, introduction & features of membership functions,

Fuzzy rule base system : fuzzy propositions, formation, decomposition & aggregation of fuzzy

rules, fuzzy reasoning, fuzzy inference systems, fuzzy decision making & Applications of fuzzy

logic.

Unit – V

Genetic algorithm : Fundamentals, basic concepts, working principle, encoding, fitness

function, reproduction, Genetic modeling: Inheritance operator, cross over, inversion & deletion,

mutation operator, Bitwise operator, Generational Cycle, Convergence of GA, Applications &

advances in GA, Differences & similarities between GA & other traditional method