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In this paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

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Effective Analysis of Lung Infection using Fuzzy Rules

Walia

Singh

Sharma

2016

IJ-AI

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<span style="font-size: 9.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; color: black; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-bidi-font-style: italic;">Soft Computing is conglomerate of methodologies which works together and provides an ability to make a decision from reliable data or expert’s experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, an algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses an output of Rulebase entered by the user to determine a level of infection.</span><div id="_mcePaste" class="mcePaste" style="position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;"><span style="font-size: 10pt; font-family: 'Times New Roman', serif;" lang="EN-IN">Soft Computing is conglomerate of methodologies which works together and provides an ability to make decision from reliable data or expert’s experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses output of Rulebase entered by user to determine level of infection.</span></div>

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A Survey on Applications of Adaptive Neuro Fuzzy Inference System

Walia¹,

Kumar²,

Singh³

2015

IJHIT

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In this paper we presented an architecture and basic learning process underlying in fuzzy inference system and adaptive neuro fuzzy inference system which is a hybrid network implemented in framework of adaptive network. In real world computing environment, soft computing techniques including neural network, fuzzy logic algorithms have been widely used to derive an actual decision using given input or output data attributes, ANFIS can construct mapping based on both human knowledge and hybrid learning algorithms. This study involves study of ANFIS strategy ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

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Effective Analysis of Lung Infection using Fuzzy Rules

Tiwari

Walia²,

Singh

et al. 2015

IJBSBT

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Soft Computing is a consortium of methodologies which works synergistically and provides an ability to make decision from reliable data or expert's experience. Nowadays different types of soft computing techniques such as neural network, fuzzy logic, genetic algorithm and hybrid system are largely used in medical areas. In this paper, algorithm for analysis of lung infection is presented. The main focus is to develop system architecture to find probable disease stage patient may have. Severity level of disease is determined by using rule base method. The algorithm uses output of Rulebase and entered by user to determine level of infection.

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Predicting the Existence of Mycobacterium Tuberculosis Using Hybrid Neuro Adaptive System

Walia¹,

Singh²,

Sharma³

2015

JCOM

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Navneet Walia

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

Effective Analysis of Lung Infection using Fuzzy Rules

A Survey on Applications of Adaptive Neuro Fuzzy Inference System

Effective Analysis of Lung Infection using Fuzzy Rules

Predicting the Existence of Mycobacterium Tuberculosis Using Hybrid Neuro Adaptive System

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