Rule Base of Fuzzy logic

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A Product Review Using Rule Base And Fuzzy Logic Approach

A Product Review Using Rule Base And Fuzzy Logic Approach

M. Tech. Computer Science and Engineering Nagpur, VIT Nagpur. Abstract: Nowadays, internet has become used for various working in life. And it is helpful for the great development in resources, communications, online resources, blogs, discussion, conference forums etc. And it is used for getting a new ideas for the identify and exact information get through the internet with help of data, texting subjects. Opinion mining is used for the get perfect data /information. And while shopping people opinion is more helpful for choosing any product. This will be get the product reviews and comments .And getting this comments and reviews determines the polarity of sentiments. And also determine the smiley. And it is product reviews and comments it compares the two and more different products, and choice the which product is best as comparison one of it. Sentiwords and smiley's using it finding the source word. And sentiment word includes the positive review, negative review, objective .Rule base and fuzzy logic approach to giving the outputs for products . A facts expressed keywords from opinions. It is helping for the getting exact reviews, comments, opinions etc.
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MODIFICATION OF FUZZY LOGIC RULE BASE IN THE OPTIMIZATION OF TRAFFIC LIGHT CONTROL SYSTEM

MODIFICATION OF FUZZY LOGIC RULE BASE IN THE OPTIMIZATION OF TRAFFIC LIGHT CONTROL SYSTEM

Modification of Fuzzy Logic Rule Base in the Optimization of Traffic Light Control System intersection that connects KRPC with the rest of the neighboring routes always experience high rate of traffic congestions during the mornings hours and evening hours of week days. This is solely due to workers going to work and closing from work and also the lifting of finished products from KRPC. The increase in motorist on the KRPC junction without a corresponding increase in the capacity of the roads constitutes a lot of menace to the surrounding area. In order to solve this menace, traffic wardens and actuated ATLS have been deployed at the intersection to reduce the problems of crashes due to impatience of motorist, robbery of motorist due to traffic jam and pollution of the area due to excessive emission of harmful gases from exhaust pipes of vehicles. However, the problem of weariness associated with the traffic wardens is a major concern and the actuated ATLS suffers from the problem of uncertainty as the arrival time of vehicles follows a Poisson distribution. The dynamic ATLS proposed in Babangida et al., (2017) has a very high size of Fuzzy Logic (FL) rule base which might result in computational burden on the traffic light system. A small size FL rule base is proposed in this paper in order to optimize the traffic light control system (TLCS).
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Comparative study of fuzzy logic speed controller in vector controlled PMSM drive: minimum number of fuzzy rule-base

Comparative study of fuzzy logic speed controller in vector controlled PMSM drive: minimum number of fuzzy rule-base

DESIGN OF Fuzzy LOGIC SPEED CONTROLLER The main goal of the control system is to track the command speed by reducing the complexity of fuzzy rule- base design of fu[r]

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Keywords: Forecasting, S&P CNX NIFTY 50, Fuzzy-logic, Fuzzy rule-base, Candlesticks, Fuzzycandlesticks, Figure 1.2: White Candle

Keywords: Forecasting, S&P CNX NIFTY 50, Fuzzy-logic, Fuzzy rule-base, Candlesticks, Fuzzycandlesticks, Figure 1.2: White Candle

Inverted Hammer Abstract: This paper proposes an easy model using which stock market forecasting can be performed. Researchers find stock markets as very dynamic and chaotic systems drawing their attention towards forecasting its movements. In this paper, a fuzzy approach is investigated for the famous candlestick method pinpointing on the Hammer formation. Modeling using fuzzy logic is done so that we can make the system understand, what we see as humans. The prediction of the future trend using the hammer candlestick technique is implemented using the fuzzy rule-base and fuzzy inference mechanism. For experimentation purpose the input for the proposed system is the real time daily and weekly data of NIFTY-50 index of National Stock Exchange of India.
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Realization of MIN-MAX Circuit for Rule Base Block of Fuzzy Logic Temperature Controller using P-Spice Simulator

Realization of MIN-MAX Circuit for Rule Base Block of Fuzzy Logic Temperature Controller using P-Spice Simulator

Srismrita Basu 1 , Subhodip Maulik 2 1,2 Institute of Engineering and Management Abstract— This paper proposes the realization of a CMOS min–max circuit for the Rule-Base block of Fuzzy Logic Temperature controller using P-SPICE. Here, we mainly deal with the analog implementation of VLSI circuit. Then digital implementation was compared with analog implementation and the advantages of the analog implementation for this particular case were presented. Popularity of the analog implementation is due to the fact of their continuous-time- processing and high frequency and low power implementation.
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RULE BASE IDS FOR APPLICATION LAYER USING FUZZY LOGIC

RULE BASE IDS FOR APPLICATION LAYER USING FUZZY LOGIC

visual.sangi@gmail.com Abstract: The objective of this paper is to develop a Fuzzy Rule-Base Based Intrusion Detection System on Application Layer which works in the application layer of the network stack. It consists of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. At application layer, HTTP traffic’s header and payload are analyzed for possible intrusion. In the proposed misuse detection module, the semantic intrusion detection system works on the basis of rules that define various application layer misuses that are found in the network. An attack identified by the IDS is based on a corresponding rule in the rule-base. An event that doesn’t make a ‘hit’ on the rule-base is given to a Fuzzy Intrusion Detection System (FIDS) for further analysis. In a Rule-based intrusion detection system, an attack can either be detected if a rule is found in the rule base or goes undetected if not found. If this is combined with FIDS, the intrusions went undetected by RIDS can further be detected. These non-intrusive patterns are checked by the fuzzy IDS for a possible attack. The non-intrusive patterns are normalized and converted as linguistic variable in fuzzy sets. These values are given to Fuzzy Cognitive Mapping (FCM). If there is any suspicious event, then it generates an alarm to the client/server. Results show better performance in terms of the detection rate and the time taken to detect. The detection rate is increased with reduction in false positive rate for a specific attack.
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Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

Copyright © 2014 Yan-Xin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability.
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2002 - Putting Fuzzy Logic to Work - An Intro to Fuzzy Rule

2002 - Putting Fuzzy Logic to Work - An Intro to Fuzzy Rule

While a statistical regression or neural network could have solved this problem, they (in contrast to the fuzzy rule-based system) require an extensive set of examples from which to learn the appropriate mapping of pixel brightness from low- contrast to high-contrast. Fuzzy rules, on the other hand, mimic an existing base of human knowledge. Construction of the fuzzy rule base does not require an extensive database of “correct” mappings from input to output (although these would be helpful in validating its performance). Instead, it requires some type of knowledge to translate
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Towards sparse rule base generation for fuzzy rule interpolation

Towards sparse rule base generation for fuzzy rule interpolation

Keywords–Sparse rule base generation, fuzzy rule interpo- lation, fuzzy rule base, fuzzy inference systems. I. I NTRODUCTION Fuzzy sets and fuzzy logic theory offer a formal way of handling vague information that arises due to the lack of sharp distinctions or boundaries between pieces of in- formation. With an inherent ability to effectively represent and reason on human natural language, fuzzy logic theory is considered as an advanced methodology in the field of control systems. The most common fuzzy models are rule- based fuzzy inference systems, each of which is composed of mainly two parts: an inference engine and a rule base (or knowledge base). The inference engines have been defined by different inference approaches, such as the Mamdani model [1] and the TSK model [2]. Although the TSK model is able to generate crisp output, the Mamdani model is more intuitive and suitable for dealing with human natural lan- guage inputs using max-min operators during the inference.
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Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation

Towards Sparse Rule Base Generation for Fuzzy Rule Interpolation

Keywords–Sparse rule base generation, fuzzy rule interpo- lation, fuzzy rule base, fuzzy inference systems. I. I NTRODUCTION Fuzzy sets and fuzzy logic theory offer a formal way of handling vague information that arises due to the lack of sharp distinctions or boundaries between pieces of in- formation. With an inherent ability to effectively represent and reason on human natural language, fuzzy logic theory is considered as an advanced methodology in the field of control systems. The most common fuzzy models are rule- based fuzzy inference systems, each of which is composed of mainly two parts: an inference engine and a rule base (or knowledge base). The inference engines have been defined by different inference approaches, such as the Mamdani model [1] and the TSK model [2]. Although the TSK model is able to generate crisp output, the Mamdani model is more intuitive and suitable for dealing with human natural lan- guage inputs using max-min operators during the inference.
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Curvature-based sparse rule base generation for fuzzy rule

interpolation

Curvature-based sparse rule base generation for fuzzy rule interpolation

Chapter 1 Introduction 1.1 Fuzzy Logic and Fuzzy Inference Systems Fuzzy logic was firstly proposed in 1965 which provides an approximate reasoning approach based on partial membership, rather than simply true or false as used in the traditional Boolean Logic. Fuzzy sets and fuzzy logic theory provide an efficient way of handling vague information that arises due to the lack of sharp distinctions or boundaries between pieces of information. With the ability to effectively represent and reason human natural language, fuzzy logic theory is considered as an advanced methodology in the field of control systems. It has been widely used in many real-world applications such as in science, engineering, business, psychology, medicine and other fields. Some early commercial applications of fuzzy systems include: fuzzy automatic transmissions and fuzzy anti-skid braking systems developed by Nissan, auto-focusing cameras by Canon, digital image stabilisers for camcorders by Matsushita, hand-writing recognition systems by Hitachi, hand- printed character recognition systems by Sony, voice recognition systems by Ricoh and Hitachi, stock-trading portfolio systems used in Tokyo stock market, Sendai station subway control systems in Japan, and so on [5–19].
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A Fuzzy Rule Base System for the Diagnosis of Heart Disease

A Fuzzy Rule Base System for the Diagnosis of Heart Disease

E.P.Ephzibah1, V. Sundarapandian[15]have proposed a neuro fuzzy expert system that finds a solution to diagnose the disease using some of the evolutionary computing techniques like genetic algorithm, fuzzy rule based learning and neural networks. Their proposed neuro-fuzzy method refers to the combination of artificial neural network and fuzzy logic based system in which the 13 attributes have been applied taken from UCI Machine learning repository. The result has helped the doctors to arrive at a conclusion about the presence or absence of heart disease in patients.
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Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic

Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic Using Fuzzy Logic

Table 2: Fuzzy Rule Base 6.4 Alarm Packet Generation Module On the basis of information passed by fuzzy verification module [9], if the fidelity level is less than the threshold fidelity level, this model generates an alarm packet with IP address of the node that is declared as black hole node.

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Title: A Review on Fuzzy Rule-Base Expert System Assessment Possibility of Allergy

Title: A Review on Fuzzy Rule-Base Expert System Assessment Possibility of Allergy

ABSTRACT- The allergy based diagnostic expert system (ABExS) is designed to help or assist the psychology doctors to diagnosing the various mental disorders related to human. ABExS can be used to perform some evaluation of patient’s physical and emotional symptoms to diagnose the particular disorder In this expert allergy system there are of different types of methods to find out about the various types of allergies. But in this will combine various diagnosis of allergy in single system. So patients have no need to go to different PR auctioneers/doctors for diagnosis. A single system will be responsible for curing all types of allergies. The Diagnosis system uses more number of variables than previous diagnosis to give more accurate results than the previous one. As a result, the decision support system will be more closely between machine and humans. ABExS using three AI techniques: Fuzzy generator, Fuzzy logic and rule based reasoning. We are going to describe a new method for creating a weighted fuzzy rule to deal with the ment5al illness. The fuzzy rule is a causal rule. Its IF part truly cause the THEN. The knowledge of human expert system in the area of mental ill and disorder is transformed and often encoded into the knowledgebase using a fuzzy logic and then provide the severity of any particular disorder.
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Implementation of genetic algorithm based fuzzy logic controller with automatic rule extraction in FPGA

Implementation of genetic algorithm based fuzzy logic controller with automatic rule extraction in FPGA

during computation. The designed fuzzy system reduces the level of complexity with a number of advantages over the original system. A fuzzy system consists of four basic blocks which are the fuzzifier, the inference engine, the knowledge base or rule base and the defuzzifier. Almost all the signals available in real world are analog in nature. To process these signals first they need to be sampled in time domain. The processing of these samples by a fuzzy logic based system involves feeding of these samples to the fuzzifier. It converts these analog samples in to appropriate fuzzy values and feeds them to the inference engine. Inference engine gives a conclusion (output) from the facts (input) and knowledge (control rules). The knowledge base contains all the rules to take the necessary decisions. The fuzzy outputs provided by the inference engine are defuzzified by the defuzzifier section in order to give final crisp output.
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Fuzzy Logic Approach for

Fuzzy Logic Approach for

The result of GA is search algorithm with innovative flair of human search. The chain codes are used to represent a boundary by connected sequence of straight-line segments of specified length and direction. Typically, the chain codes representation is based on 4 or 8 connectivity of the segments. The direction of each segment is coded by using a numbering such as shown in Figure 1. Chain rule is base on two different ways of computing perimeters. The value of perimeters is the actual boundary pixels of the object. The value of outside perimeter is actually required of the mobile robot to move to boundary pixels that have been traverse before.
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Tendering Process: Improvement of Analysis and Evaluation of Tenders based on the Use of Fuzzy Logic and Rule of Proportion

Tendering Process: Improvement of Analysis and Evaluation of Tenders based on the Use of Fuzzy Logic and Rule of Proportion

5.2.1.3 Definition of fuzzy sets, linguistic terms and membership functions The fuzzy sets have to be defined for each input variable and for each output variable. It allows to introduce the graduality by defining qualitative values (magnitudes) to consider, specifying when they are true or false. These values will become the linguistic terms of the rules base.

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Fuzzy Logic Fuzzy Rules and Fuzzy Rule Based Systems

Fuzzy Logic Fuzzy Rules and Fuzzy Rule Based Systems

• The input of the aggregation process is the list of clipped or scaled consequent membership functions, and the output is one fuzzy set for each output variable... Step 4: Defuzzific[r]

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Association Rule Mining with Fuzzy Logic: an Overview

Association Rule Mining with Fuzzy Logic: an Overview

association rule mining (Fuzzy ARM). An association relationship can help in decision making for the solution of a given problem. Association Rule Mining (ARM) with fuzzy logic concept facilitates the straightforward process of mining of latent frequent or repeated patterns supported their own frequencies within the sort of association rules from any transactional and relational datasets containing items to indicate the foremost recent trends in the given dataset. These fuzzy association rules use either for physical data analysis or additionally influenced to compel any mining task like categorization (classification) and collecting (clustering) which helps domain area experts to automate decision-making. Within the concept of data mining, usually fuzzy Association Rule Mining (FARM) technique has been comprehensively adopted in transactional and relational datasets those datasets containing items who has a fewer to medium quantity of attributes/dimensions. Classical association rule mining uses the concept of crisp sets. AS it uses crisp sets, classical association rule mining has number of drawbacks. To conquer drawbacks of classical association rule, the concept of fuzzy association rule mining is introduced. There is an enormous range of various sorts of fuzzy association rule mining algorithms are available for research works and day by day these algorithms are getting better. However at identical time problem domain also becoming more complex in nature so that research work is still going on continuously. In this paper, I have studied several well-known methodologies and algorithms for fuzzy association rule mining.
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Fuzzy modelling using a simplified rule base

Fuzzy modelling using a simplified rule base

The conventional conjunctive form of if-then rule assumes that both input variables-(in this case, the quality of service andjbod) must be present in order to contribu[r]

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