Analysis of EM scattering in Waveguide Filter using Neural Network
This paper discusses the application of Neural Network (NN) technique in the modeling of a typical electromagnetic (EM) field scattering problem in a waveguide filter structure [1]. The structure under consideration is a rectangular waveguide with four dielectric circular rod inserted symmetrically. The Neural Network takes into consideration the geometrical and material parameters of the EM model of the structure and the reflection coefficient values for different frequencies in the frequency band of interest. The Neural Network is trained off line using training data sets generated by the image theory. The work is to find out an acceptable Neural Network model of the waveguide structure having four dielectric post, verification using Method of moment by simulation and the corresponding filter structure practically implemented in hardware for verification.
Implementation of Simulink in Protection of Transmission Lines
High voltage transmission lines are used to transmit electrical energy from source to substa tions. If any fault and disturbance are generated in the tran smission lines and not detected, located and elimin ated quickly, it may cause instability in the system. To find the exact location of fault occurring in transmission lines may be calculated by some conventional method s. This paper presents the implementation of Simulink/MATLAB for finding the exact location of f ault in power system.From the help of Simulink circuit diagram we will calculate the changes of voltage, c urrent and power when the fault is occurs in phase A, phase B and phase C either with respect to ground or with respect to any two phases. Simulink results will shown in this paper. KeywordsANN (Artificial neural network), Simulink , High voltage transmission lines, Fault. Introduction Work on artificial neural networks commonly referre d to as “neural networks”, has been motivated right from its inception by the recognition that the human brain c omputers in an entirely different way from the conv e tional digital computers. The brain is a highly complex, n onlinear and parallel computer. It has the capabili ty to organize its structural constituents known as neurons, so as to perform certain computations (e.g. pattern reco gnition, perception and motor control) many times faster tha n t e fastest digital computer in existence today.[ 1] A neural network is a massively parallel distribute d processor made up of simple processing unit that has a natural propensity for storing experimental knowled g and making it available for use. It resembles th brain in two respects: Knowledge is acquired by the network from its envir onment through the learning process. Inter-neuron connection strengths known as synaptic we ghts are used to store the acquired knowledge (i) Artificial intelligence: Designing of intelligence computer system from char acteristic associated with intelligence in human behavior [1] Example: 1. Neural Network 2. Fuzzy Logic 3. Expe rt system 4. Probabilistic reasoning. Types: 1. Hard computing 2. Soft computing Characteristics: 1. Cognition 2. Logical Interfac e 3. Pattern Recognition Human brain has two properties: Human brain is getting experienced to adapt themse lve to their surrounding environments. So as a result the inform ation processing capability of the brain is rendere , when this happen the brain becomes plastic. 1. Plastic: Capability to process information, ca pability of adding. Must preserve the information i t has learn previously. 2. Stable: Remain stable when it is presented wit h irrelevant information, useless information. *. Synapses with large area are excitatory (+Ve wei ghts) & with small area are inhibitory (-Ve weights ). IJAEEE ,Volume1, Number 2 Isha Awasthi et al. ISSN: 2319-1112 /V1N2: 195202 © IJAEEE *. Synapses of the neuron are modulated as weights. (Strength of the connection) *. Biological neuron receive all inputs through den rites, sum them & produces an output. If the sum i s greater then the threshold value, then input signals are passed to the cell body. *. NN can mapped input patterns into output pattern s. *. NN’s are robust systems. They can recall full pa tterns from incomplete pattern or noise channel. *. Mc Culloh Pits neuron was centered on the idea t hat a neuron will fire an impulse only if the thres hold value is exceed. Total input (I) receiv by the soma To generate the final output Y sum is passed throug h a nonlinear filter called activation filter. (ii) Simulink Modelling Diagram: Modern power systems are increasingly complex and t he use of computers to analyze and simulate their performance is now commonplace. There are many comp uter ackages on the market. Unfortunately, it is n ot all about entering data, but how the input data was arr ived at. It is also important to distinguish betwee n good and bad data. Simulink figure; is made of generators, motor s, transmission lines, transformers, high speed dig ital relays and circuit breakers. For each of these items data is r equi ed before meaningful results can be obtained, SIMULINK is mouse-driven. The modeling of any system is carried out in the model window through identification and connection of the blocks that make up the system un der consideration. Usually, it is important to mode l the system first on paper as to have a general knowledge of th e various components that make up the system. (iii) Circuit Description:-
Millimeter wave RF front end design using neuro-genetic algorithms
This paper presents neural network and genetic algorithm based modeling and design of millimeter wave RF front end circuits. The neuro-genetic design methodology is composed of two stages. Stage one consists of the development of an accurate neural network model for the microwave filters from the measured data. This model can be used to perform sensitivity analysis and derive response surfaces. In the second stage, the neural network model is used in conjunction with genetic algorithms to synthesize millimeter wave devices with desired electrical specifications. The synthesis methodology uses an accurate model that accounts for the manufacturing variations and parameter indeterminacy issues. Furthermore, the genetic synthesis algorithm uses a priority scheme to account for tradeoffs among various electrical characteristics to provide the best design. This method has been used to synthesize mm-wave low pass and band pass filters. The electrical response obtained from the layout parameters predicted by the method matches the desired electrical characteristics within 5%. The generic nature of the technique suggests potential extension to other mm-wave front ends, such as antennas, diplexers and baluns.