Soft computing techniques are set of nature-inspired computational methodologies which consider the computational utility of architectures and algorithms inspired by nature and their applications for solving mathematical problems. These are basically stochastic search techniques and are computationally efficient as they possess the ability of implementing distributed computing. Unlike traditional hard computing techniques, these set of techniques have the ability to deal with partial and noisy sets of data, tolerance to initial impression and providing a robust solution with the requirement of minimal computing resources. Over last few decades soft computing methods have successfully been applied to solve many complex problems related to engineering fields. Dote and Ovaska [1] provided a comprehensive review on application of soft computing methodologies for solving complex industrial problems including its application to the aerospace industry, communications systems, consumer appliances, electric power systems, manufacturing automation and robotics, power electronics and motion control, process engineering, and transportation planning. Gao and Ovaska [2] made a review on applications of soft computing in motor fault diagnosis, Hajela [3] in multidisciplinary aerospace design, Flintsch and Chen [4] in infrastructure management, Nikravesh [5] in reservoir characterization, Avineri [6] in traffic and transport management systems, Oduguwa et al. [7] in manufacturing industry, Saridakis and Dentsoras [8] in engineering design which addresses issues such as, the design knowledge representation (modeling), the search for optimal solutions and the retrieval of pre-existing design knowledge and the learning of new knowledge, Chandraseka... ... middle of paper ... ...where fusion computation was performed. The final fusion decision was made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. For the demonstration of the process they used a numerical model of 7-degree of freedom building model. Again, Hakim and Razak [83] applied adaptive neuro-fuzzy inference system (ANFIS) for damage identification in steel girder bridges using experimental natural frequencies data. Further, Dash [84] developed fuzzy controller with Gaussian membership functions for multi-crack detection in beam structures. The changes in the natural frequencies and mode shapes due to crack as calculated from finite element simulation studies were fuzzified to construct fuzzy controller. They experimentally validated the feasibility of the method using a cantilever beam made up of aluminum.
The advent of neural net with the seminal work of Hopfield , popularized the use of machine intelligence techniques in the pattern recognition. However, the dense and inherent structure of neural networks is not suitable for VLSI implementation. So, researchers in the neural network domain tried to simplify the structure of the neural network by pruning unnecessary connections. Simultaneously, the CA research community explored the advantages of the sparse network structure of cellular automata for relevant applications. The hybridization of cellularity and neural network has given rise to the popular concept of cellular neural networks.
EEE 4620: Lab for students of 3rd year B.Sc. in Elect. Engg. program, related with EEE 4619.(Summer Sem 2004)
All the data which is being used will be processed based on knowledge base which will help Autolib develop their ideas and help them to achieve their goals. Finally we can say that the Autolib has used all the features of the knowledge base intelligence successfully to achieve the success for their organization and achieve a simple solution to solve complex problems. We can assume that Autolib along with IER has used most of the principles of KBS not only for designing a new solution but also delivered it in the time span of 7 months which has received high recognition among people and help them commute easily and save around 90% of their earnings by subscribing with their organization rather than maintaining a car and providing customers with more options, programs which support the vision of a greener city with less noise and air pollution.
[7] K. Anil Jain, Jianchang Mao and K.M. Mohiuddi, 1996. “Artificial Neural Networks: A Tutorial”, IEEE Computers, pp.31-44.
Often it is enough is to solve problems in a very specific domain. Expert systems are often called upon when this type of intelligence is desired. Knowledge based systems have enjoyed commercial success and have shown promise as candidates for practical intelligent systems of the near future [1].
The case based reasoning system proposed here mimics the human decision making process by learning from previous experience and using the knowledge to solve current problem. This system will utilize previous adverse episodes and their solutions to prevent reoccurrences, and also to detect the oc...
Project management has been firmly established as a concept for organizing, innovative as well as strategic endeavors. Software development organization follows some process when developing a software product. A key component of any software development process is the software development methodology on which the process is based. The particular methodology can significantly affect overall life cycle cost associated with a software product. The selection of the methodology is also based on the some predefined basic characteristics of the product under design. In this paper a fuzzy logic based approach is presented to assist organizations in making the decision regarding which software development methodology to select from Rational Unified
This Chapter presented a description of Knowledge Acquisition and Knowledge Representation in Expert Systems. This chapter will explain the knowledge Acquisition and Representation methods, production rules and frames. The section below explains the concept of Production rule, their training and learning. Also this chapter discusses methods of Knowledge Acquisition and Knowledge Representation.
The fuzzy is basic set of rules which is based on system error and change in error which expert advice into automatic control condition for self adaptive controller. Fuzzy represents a sequence of control mechanism to adjust the effect of certain system stimulations. It reflects the expert conditions in to appropriate control design.
These crisp measurements can be obtained from an expert knowledge of the system. (2) Fuzzification it is generally the process of altering a real scalar value into a fuzzy value. This can be achieved by the different type of fuzzifies or membership functions. In this phase, the crisp variables eω (k) and deω (k) are transformed in to fuzzy variables e ω (error) and de ω (change in error) respectively. Each fuzzy variable is an associate of the subsets with a level of either membership μ varying between 0 (non-member) and 1 (full-member). The Fuzzification block Computes the values of input variables.(3) Fuzzy Inference engine Fuzzy inference system (FIS) employs a fuzzy set theory for portraying of the inputs with the outputs. Mamdani type of inference system is used in this method. In this stage, the variables error and change in error are processed by an inference engine that accomplished 7 RULES. These rules are confirmed using the knowledge of the system performance by the control engineers. Each rule is manifested in the form of the following example: IF (Error is Negative Large) THEN (Control signal provided will also be a negative large to compensate the Error). (4) Knowledge Based / Rule Based A decision building logic replicating a human decision ideology reduces the
Maqsood, T., Finegan, A.D., and Walker, D.H., 2001. Five case studies applying soft systems methodology to knowledge management. [online] Available at: http://eprints.qut.edu.au/27456/ [Accessed 8 April 2014]
explicitly relates cybernetics to the theory of automatic control and to the physiology of the
The most basic elements of a neural network, the artificial neurons, are modeled after the neurons of the brain. The "real" neuron is composed of four parts: the dendrites, soma, axon, and the synapse. The dendrites receive input from other neuron's synapses, the soma processes the information received, the axon carries the action potential which fires the neuron when a threshold is breached, and the synapse is where the neuron sends its output, which are in the form of neurotransmitters, to the dendrites of other neurons. Each neuron in the human brain can connect with up to 200,000 other neurons. The power and processing of the human brain comes from multitude of these basic components and the many thousands of connections between them.
Choosing a career is very important in a person’s life. Over the past two decades, many professions have change significantly with the influx of technological developments. One needs to think about the things that interest them and what kind of lifestyle they want to have. Some things a person should think about are what qualifications are needed, what type of training is necessary, and the future need of the career they choose. Some other things to consider would be how much money they will make, what is the probability of advancement, and does the career satisfy their need for an enjoyable life.
The range of task environments that can be characterized by well - defined problems is vast. We can distinguish between so - called, toy problems, which are intended to illustrate or exercise various problem - solving methods, and so - called real - world problems, which tend to be more difficult and whose solutions people actually care about. In this section, we will give examples of both. By nature, toy problems can be given a concise, exact descri ption. This means that they can be easily used by different researchers to compare the performance of algorithms. Real - world problems, on the other hand, tend not to have a single agreed - upon description, but we will attempt to give the general flavor of t heir formulations.