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.
Putting the ' Smarts' into the Smart Grid: A Grand Challenge for Artificial Intelligence. (2012).
Abstract:- This paper presents a brief idea about data mining, data mining technology, and big data. The applications regarding data mining will also be discussed briefly. The main cause of data mining is to get different ideas, how to access big data by different tools.
Chapter-5 describes my whole work i.e. generation of testcases using genetic algorithm. Process of the generation of test cases is given. How the factors described can help in finding fitness function. Operators used by genetic algorithms are described.
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...
For the development of Big Data, Data Mining and Predictive Analytics applications, several methodologies and techniques routed to the control and post-analysis of info-data have been generated in different fields. Those methodologies and techniques allow a better use of info-data to solve a specific problem. Some fields, in which Big Data has developed, both in public and private, are health and science, economics, and business and management. Taking these into account, we can define and classify the following applications:
Various learning situations may dictate differing learning processes. The three that will be briefly highlighted in this paper are; learning by induction, through the use of decision rules or decision trees; learning by discovery; and learning by taking advice, explanation-based generalization. The concept of multi-strategy learning in order to handle more complex problems will also be examined.
[2]S.M Mohd, I.Zuwairie, U.Satomi, O.Osamu, K. Marzuki(2005). DNA Computing for Complex Scheduling Problem[Online]. Available : http://www.isc.meiji.ac.jp/~onosemi/IADC/ICNCSaufee.pdf
Therefore, to determine the material handling system is very important for reduce cost and increased profits. The fact material handling systems represent a major part of the total manufacturing cost make necessity to choose adequately the material handling system when a manufacturing system is designed. One of the most successful applications of experts systems is selection of equipment for material handling (SEMH). SEMH lookups the knowledge bottom to be able to suggest the degree associated with mechanization, and also the type of product coping with equipment to become utilized, according to some traits. Fisher, Farber, and Kay (1998) have introduced MATHES: material handling equipment selection expert systems for the selection of a material handling equipment from 16 possible choices. MATHES as well as 172 principles takes course, product stream quantity, product sizing's in addition to distance concerning sectors because variables. Swaminathan, Matson, and Mellichamp (1992) have developed EXCITE: expert consultant for in-plant transportation equipment addressing 35 equipment
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.
Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize certain patterns and situations and apply these to certain priorities and output appropriate results. These types of neural networks can be used in many important situations such as priority in an emergency room, for financial assistance, and any type of pattern recognition such as handwritten or text-to-speech recognition.
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.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
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]
Fortunately, during under-graduation, I got an opportunity to detect the optimum path of a process through my project “Design and Development of PCB Dual Head Drilling Machine”. Additionally, I became aware of a new subject called Six Sigma which aided me in intertwining new optimization techniques into my project to make the process effective. I optimized the creation...