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Feb 20, 2019 · This document describes a seminar presentation on the design and application of an analog fuzzy logic controller. It provides an overview of fuzzy logic control systems, including why they are used, their typical architecture, and how they work.
- Fuzzy logic control | PPT - SlideShare
Fuzzy logic systems use fuzzy membership functions and fuzzy...
- Fuzzy logic control | PPT - SlideShare
Oct 3, 2019 · The document outlines the steps to build a fuzzy logic system and describes various applications of fuzzy logic controllers in areas like temperature control, motor speed control, washing machines, and air conditioners.
general fuzzy controller consists of four modules: a fuzzy rule base, a fuzzy inference engine, a fuzzification module, and. a defuzzification module. As shown in Figure 1, a fuzzy controller operates by repeating a cycle of the following four steps :
Apr 3, 2020 · Fuzzy logic systems use fuzzy membership functions and fuzzy "IF-THEN" rules to map inputs to outputs. They include fuzzification of inputs, an inference system to evaluate rules, aggregation of outputs, and defuzzification to produce a crisp output.
- B. Tech III-II Sem. (EEE) L T P C 3 1 0 3 15A02604 NEURAL NETWORKS & FUZZY LOGIC ( CBCC-I )
- INTRODUCTION TO ARTIFICIAL INTILLEGENCE
- UNIT – II ARTIFICIAL NEURAL NETWORKS
- UNIT – III ANN APPLICATIONS TO ELECTRICAL SYSTEMS
- UNIT – IV FUZZY LOGIC
- Artificial Neural Networks and their Biological Motivation Artificial Neural Network (ANN)
- (naive) structure of biological neurons
- Taxonomy of neural networks
- Feed forward supervised networks
- Feed forward unsupervised networks
- Feedback networks
- Models of artificial neurons
- Types of activation functions
- A step function
- A step function with bias
- Sigmoidal functions
- Radial-Basis Functions
- Perceptron as a Pattern Classifier
- ADALINE — The Adaptive Linear Element
- Feedforward Multilayer Neural Networks
- Multilayer perceptrons
- Classification model, Features and Decision regions:
- Discriminant Functions:
- UNIT-III ASSOCIATIVE MEMORIES ASSOCIATIVE MEMORIES:
- BASIC CONCEPTS:
- PERFORMANCE ANALYSIS OF RECURRENT AUTOASSOCIATIVE MEMORY:
- Advantages and Limitations
- FUZZY SET THEORY
- Union
- Intersection
- Fuzzy Set Operations
- Intersection
- Complement
- Standard fuzzy operations
- RELATIONS
- Crisp and Fuzzy Relations
- Cartesian product
- Propositional Logic
- Fuzzy Logic Controller: Fuzzification
Course Objective: The objectives of the course are to make the students learn about: Importance of AI techniques in engineering applications Artificial Neural network and Biological Neural Network concepts ANN approach in various Electrical Engineering problems Fuzzy Logic and Its use in various Electrical Engineering Applications UNIT – I
Introduction and motivation – Approaches to AI – Architectures of AI – SymbolicReasoning System – Rule based Systems – Knowledge Representation – ExpertSystems.
Basics of ANN - Comparison between Artificial and Biological Neural Networks – BasicBuilding Blocks of ANN – Artificial Neural Network Terminologies – McCulloch PittsNeuron Model – Learning Rules – ADALINE and MADALINE Models – PerceptronNetworks – Back Propagation Neural Networks – Associative Memories.
ANN approach to: Electrical Load Forecasting Problem – System Identification –Control Systems – Pattern Recognition.
Classical Sets – Fuzzy Sets – Fuzzy Properties and Operations – Fuzzy Logic System– Fuzzification – Defuzzification – Membership Functions – Fuzzy Rule base – FuzzyLogic Controller Design.
There is no universally accepted definition of an NN. But perhaps most people in the field would agree that an NN is a network of many simple processors (“units”), each possibly having a small amount of local memory. The units are connected by communication channels (“connections”) which usually carry numeric (as opposed to symbolic) data, encoded ...
biological neuron, or a nerve cell, consists of Fig: The pyramidal cell— a “prototype” of an artificial neuron. synapses, dendrites, the cell body (or hillock), the axon. Simplified functions of this very complex in their nature “building blocks” are as follow: The synapses are elementary signal processing devices. A synapse is a biochemical device...
From the point of view of their active or decoding phase, artificial neural networks can be classified into feed forward (static) and feedback (dynamic, recurrent) systems. From the point of view of their learning or encoding phase, artificial neural networks can be classified into supervised and unsupervised systems.
This network is typically used for function approximation tasks. Specific examples include: Linear recursive least-mean-square (LMS) networks Back propagation networks Radial Basis networks
These networks are used to extract important properties of the input data and to map input data into a “representation” domain. Two basic groups of methods belong to this category Hebbian networks performing the Principal Component Analysis of the input data, also known as the Karhunen-Loeve Transform. Competitive networks used to performed Learnin...
These networks are used to learn or process the temporal features of the input data and their internal state evolves with time. Specific examples include: Recurrent Back propagation networks Associative Memories Adaptive Resonance networks
Artificial neural networks are nonlinear information (signal) processing devices which are built from interconnected elementary processing devices called neurons. An artificial neuron is a p-input single-output signal processing element which can be thought of as a simple model of a non-branching biological neuron. Graphically, an artificial neuron...
Typically, the activation function generates either unipolar or bipolar signals.A linear function: y = v. Such linear processing elements, sometimes called ADALINEs, are studied in the theory of linear systems, for example, in the “traditional” signal processing and statistical regression analysis.
Unipolar: Such a processing element is traditionally called perceptron, and it works as a threshold element with a binary output.
The bias (threshold) can be added to both, unipolar and bipolar step function. We then say that a neuron is “fired”, when the synaptic activity exceeds the threshold level, _. For a unipolar case,
The hyperbolic tangent (bipolar sigmoidal) function is perhaps the most popular choice of the activation function specifically in problems related to function mapping and approximation.
Radial-basis functions arise as optimal solutions to problems of interpolation, approximation and regularization of functions. The optimal solutions to the above problems are specified by some integro-differential equations which are satisfied by a wide range of nonlinear differentiable functions Typically, Radial-Basis Functions '(x; ti) form a fa...
single perceptron classifies input patterns, x, into two classes. A linear combination of signals and weights for which the augmented activation potential is zero, ˆv = 0, describes a decision surface which partitions the input space into two regions. The input patterns that can be classified by a single perceptron into two distinct classes are cal...
The Adaline can be thought of as the smallest, linear building block of the artificial neural networks. This element has been extensively used in science, statistics (in the linear regression analysis), engineering (the adaptive signal processing, control systems), and so on. In general, the Adaline is used to perform linear approximation of a “sma...
Feedforward multilayer neural networks were introduced in sec. 2. Such neural networks with supervised error correcting learning are used to approximate (synthesise) a non-linear input-output mapping from a set of training patterns. Consider a mapping f(X) from a p-dimensional domain X into an m-dimensional output space D.
Multilayer perceptrons are commonly used to approximate complex nonlinear mappings. In general, it is possible to show that two layers are sufficient to approximate any nonlinear function. Therefore, we restrict our considerations to such two-layer networks. The structure of each layer has been depicted in Figure. Nonlinear functions used in the hi...
A pattern is the quantitative description of an object, event or phenomenon. The important function of neural networks is pattern classification The classification may involve spatial and temporal patterns. Examples of patterns are pictures, video images of ships, weather maps, finger prints and characters. Examples of temporal patterns include spe...
Let us assume momentarily, and for the purpose of this presentation, that the classifier has already been designed so that it can correctly perform the classification tasks. During the classification step, the membership in a category needs to be determined by the classifier based on the comparison of R discrirninant functions gl(x), g2(x), . . . ,...
An efficient associative memory can store a large set of patterns as memories. During recall, the memory is excited with a key pattern (also called the search argument) containing a portion of information about a particular member of a stored pattern set. This particular stored prototype can be recalled through association of the key pattern and th...
Figure shows a general block diagram of an associative memory performing an associative mapping of an input vector x into an output vector v. The system shown maps vectors x to vectors v, in the pattern space Rn and output space Rm, respectively, by performing the transformation The operator M denotes a general nonlinear matrix-type operator, and i...
In this section relationships will be presented that relate the size of the memory n to the number of distinct patterns that can be efficiently recovered. These also depend on the degree of similarity that the initializing key vector has to the closest stored vector and on the similarity between the stored patterns. We will look at example performa...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Establishes the fact base of the fuzzy system. It identifies the input and output of the system, defines appropriate IF THEN rules, and uses raw data to derive a membership function. Consider an air conditioning system that determine the best circulation level by sampling temperature and moisture levels. The inputs are the current temperature and m...
Fuzzy logic controllers are special expert systems. In general, a FLC employs a knowledge base expressed in terms of a fuzzy inference rules and a fuzzy inference engine to solve a problem. We use FLC where an exact mathematical formulation of the problem is not possible or very difficult.
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Fuzzy Control of the Distance Between Two Cars Visualization of 2 INPUTS: D and v , and 1 OUTPUT B is possible. For more inputs everything remains the same but visualization is not possible.
