IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _______________________________________________________________________________________ Volume: 03 Issue: 08 | Aug-2014, Available @ http://www.ijret.org 26 ADAPTIVE NEUROFUZZY SYSTEM FOR ASTHAMA Shashank Bhardwaj 1 , Niraj Singhal 2 , Neeraj Gupta 3 1 Shobhit University, Meerut, India 2 Shobhit University, Meerut, India 3 KIET, Ghaziabad, India Abstract The problem of health monitoring has been taken as it is one of the challenging problems in rural areas where people many times do not get proper treatment and are not financially sound to visit doctors in city. Asthma is a prevalent disease; many lives are lost due to lack of proper treatment which in turn can be saved if proper prognosis is done in time. In this study of neuro fuzzy network, a detailed study has been done on the various schemes and strategies that are part of neural networks. The world today is not based only on discrete concepts but on fuzzy concepts, which made us research on the world of soft computing and finally a health monitoring system based on neural network is developed. Our objective is to study the various techniques and algorithms of neural network and to find the most efficient technique to implement this problem of health monitoring. The estimation of information about disease is based on the variables that affect its state. --------------------------------------------------------------------***------------------------------------------------------------------ 1. INTRODUCTION Fuzzy Logic has emerged as one of the active area of research activity particularly in control system application. Fuzzy logic is a powerful method of reasoning when mathematical models are not available and input data are imprecise. Its applications, mainly to control are being studied throughout the world by control engineers. Wherever logic in the spirit of human thinking can be introduced, fuzzy logic finds extreme application there. Sacrificing some amount of information we get a more robust summary of the information .What this really mean? Though we are conditioned to think in precise quantities, at a subconscious level, we think and take actions that are fuzzy in nature. And that is the way we perceive the nature and react to it. Before going to into the details, let‟s look how fuzzy logic has become a household jargon. Though fuzzy logic originated in USA some 30 years back, the researchers there were skeptic about its applicability in real world applications and some even scoffed it off as nothing but probability. On the other hand, the Japanese was closely the pioneering work done by Mamdani and his associates in steam engine control and started applying fuzzy control even too consumer goods like cameras, air conditioners, vacuum cleaners etc. Thus fuzzy based products became highly competitive due to better performance, high reliability, robustness, low power consumption, cheapness etc. One of landmark success of fuzzy control was complete automation of subway train‟s drive control system in Japan. With fuzzy logic getting a wider acceptance in recent years, it is predicted that by the end of decade fuzzy logic will replace most of convention logic. Many projects which were nearly impossible earlier are now finding a new way out by fuzzifying them. In the classical control paradigm, much stress is laid on the precision of input, the intermediate steps that process them, and modeling of the system in question. In spite of this we observed that many a time such sophisticated classical controllers developed often find it difficult to perform in real world control problems. Because the real world is so complex that regardless of the complexity of our model of the problem and the care taken to design such models, there exist so many parameters that not been properly accounted for and many more of which are totally ignorant of. Whereas a fuzzy logic solution is tolerant to the imprecision in the inputs and the model of system and still produce an output that is desired out of the system. This was put in a more effective way by Lofti A. Zadeh, the father of fuzzy set theory, when he said “Most application of fuzzy logic exploit its tolerance for imprecision. Due to being costly precision, minimization of precision is needed for performing a task.” The thinking process involved in fuzzy realm is not complex. It is simple, elegant and easily applicable. The simplicity arises because it eludes mathematics to a great extent and elegance lies in its expressiveness. Even a person who does not know anything about camera operation can design fuzzy based controller for it with the help of expert camera operator. 2. FUZZY SET VERSUS CRISP SET The main objective behind fuzzy logic is to represent and reason with some particular form of knowledge expressed in a linguistic form. However, when using a language oriented approach for knowledge representation, one has to build a conceptual framework to tackle its inherent vagueness. The traditional or crisp sets are based on a two value logic: objects are either members or not members. Every individual object is assigned a membership value μ of either