Soft Computing


Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems. In effect the role model for soft computing is the human mind. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Probabilistic Reasoning (PR), with the latter subsuming belief networks and parts of learning theory.


Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.

Types Of Soft Computing:

1. Artificial Neural Networks:

 An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.

2. Fuzzy Logic:

FL is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstationbased data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster.

3. Genetic Algorithms In Evolutionary Computation:

A genetic or evolutionary algorithm applies the principles of evolution found in nature to the problem of finding an optimal solution to a Solver problem. In a "genetic algorithm," the problem is encoded in a series of bit strings that are manipulated by the algorithm; in an "evolutionary algorithm," the decision variables and problem functions are used directly. Most commercial Solver products are based on evolutionary algorithms.



4. Support Vector Machines:

Are a set of related supervised learning methods used for classification and regression. In simple words, given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. More formally, a support vector machine constructs a hyperplane set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training datapoints of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.

Application Area Of Soft Computing:

1. Wireless Communication:

Applications of soft computing in wireless communication covers broad area of resource allocation, Handoffs, networking optimization, power control, prediction etc. Artificial Neural Network and Fuzzy Logic methods of soft computing involves in resource allocation i.e. by using ANN technique, bandwidth allocation schemes utilization for mobile networks maximizes and minimizes bandwidth allocation for individuals. An algorithm based system can be used to achieve fast and reliable solutions for dynamic resource allocation. Evolutionary Computing method of Soft Computing uses a soft computing algorithm to achieve power control in WWAN. The algorithm helps in reducing mobile terminal power consumption and increasing the cellular network capacity. Security in WLAN is also enhanced by using Artificial Neural Network and Fuzzy Logic methods. DoS and MITM attacks are common in wireless local area network. A Soft Computing is designed to minimize the authentication frauds and detection delays.



2. Consumer Appliances:

The field of consumer appliances activities is associated to practical product development. There is a huge scope of fuzzy logic, neural networks which have already brought artificial intelligence in home appliances. More recently, evolutionary computation has also shown remarkable identity in this field. These techniques are used for various applications like washing machines, heaters, refrigerators, microwaves and many more.

3. Robotics:

Robotics is an emerging field which is based on human thinking and behavior. Fuzzy Logic and Expert System techniques integrate in a way to develop useful real world applications. Also neuro-fuzzy system hybridization is the representation of fuzzy logic with learning capabilities. Neuro fuzzy approach learns obstacle avoidance and wall-following behavior on a small size robot. Present day intelligence is considered to be interactive information processing among humans and artificial objects. Intelligence is human like information processing and adaptation to environment by learning, evolution and prediction. Soft Computing is widely used in this field.



4. Transportation:

Soft Computing is applicable in constructing intelligent vehicles and provide efficient environment to each other i.e. to machines and drivers. Intelligent vehicle control requires recognition of the driving environment and planning of driving that is easily acceptable for drivers. The field of transportation deals with passengers, logistics operations, fault diagnosis etc. Fuzzy Logic and Evolutionary Computing are often used in elevator control systems.

5. Healthcare:

Health care environment is very much reliant to on computer technology. With the advancement in computer technology, the use of Soft Computing methods provide better and advance aids that assists the physician in many cases, rapid identification of diseases and diagnosis in real time. Soft Computing techniques are used by various medical applications such as Medical Image Registration Using Genetic Algorithm, Machine Learning techniques to solve prognostic problems in medical domain, Artificial Neural Networks in diagnosing cancer and Fuzzy Logic in various diseases.



6. Association Rule:

Association rules are the describing rules that associate relationship among different attributes. The use of fuzzy technique has been considered the key features of data mining systems. Neural Network contributes towards the data mining from rule extraction and clustering. Neural Network has also been used for a variety of classification and regression tasks. In addition, combination of neural network and fuzzy logic is one of the most popular hybridization that integrates the merits of neural and fuzzy approaches.




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