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feed forward back propagation (FFBP) and radial basis function(RBF) neural network algorithms are given in table 5.2 for analysis of band pass FIR filter with hanning window. Table 5.2 Comparison of mean square error using FFBP and RBF neural network algorithms used for cut off frequency calculation of band pass FIR digital filter with hanning window Test input (Filter Coefficient) hanning Window (actual cut off Frequency) Output of Artificial Neural Network (calculated cut off frequency) Mean Square

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1 Training using Feed Forward Back Propagation (FFBP) artificial Neural Network Algorithm for kaiser Window Figure 5.11: trained network for kaiser window with FFBP Figure 5.12: performance plot for kaiser window with FFBP In above figure 5.12, it shows the performance plot of a FFBP neural network. This plot is result of MLP training algorithm. Here after 7 epochs the mean square error is almost 0. That means the network is trained. Figure 5.13: regression plot for kaiser window with FFBP Here

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crucial step in medical image processing. We have used modified Radial basis function to segment the tumor. It proves to be best option then existing algorithms. RADIAL BASIS FUNCTION:

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1. Title:- Modeling of fractal antenna using Artificial Neural Network. 2.Introduction:- In high-performance spacecraft, aircraft, missile and satellite applications, where size, weight, cost, performance, ease of installation, and aerodynamic profile are constraints, low profile antennas may be required. Presently, there are many other government and commercial applications, such as mobile radio and wireless communications that have similar specifications. To meet these requirements, micro strip

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AND ANALYSIS OF LOW PASS FIR DIGITAL FILTER USING ARTIFICIAL NEURAL NETWORK In proposed work, low pass FIR digital filter has been designed and analyzed using artificial neural network (ANN) with Bartlett-Hanning and Blackman-Harris window function. 4.1 Introduction In proposed work, three neural network algorithms, namely feed forward distributed time delay (FFDTD), feed forward back propagation (FFBP) and radial basis function(RBF) is preferred for estimation of the cut off frequency of low pass

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making problem, having wide practical application in various fields. Extreme Learning Machine (ELM) pro- posed by Huang et al.[1], is an effective machine learning technique for real valued classification. ELMis a single hidden layer feedfo5 rward network in which the weights between input and hidden layer are initialized randomly. ELM uses analytical approach to compute weights between hidden and output layer [2], which makes it faster compared to other gradient based classifiers ([3, 4]). Various

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The authors have proposed wavelet based image processing technique and neural network to develop a method of online identification of pest damage in fruit in orchards [11]. Three pests that are predominant in orchards were selected as the parameter for the research: the leaf-roller, codling moth, and apple leaf curling midge. Fast wavelet transform with distinct set of Doubenchies wavelet was used to extract the significant features. To get better the related images, the search is done in two stages

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743–750 Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks Changhao Xia a,b,*, Jian Wang b,*, Karen McMenemy c a College of Electrical Engineering and Information Technology, China Three Gorges University, Yichang Hubei 443002, China School of Mechanical and Aerospace Engineering, Queen’s

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designing Modern high-fidelity filters. During past few years, various contributions have been made in literature on the comparison of FIR filter using neural network. Accurate analysis of comparison of different window function requires a cumbersome

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SIMULATION / EXPERIMENTAL RESULT 5.1 RESULTS USING NEURAL NETWORK – GA STRUCTURE : - To check the performance of genetic neural network .Firstly the dataset normalized and divided into groups of training and testing set .Randomly generated the groups by splitting process with specific proportion at each time(50% to 50%,10% to 90%,30% to 70%) to formdifferent training /testing groups. GA applied five times for finding the optimal network topology For each testing /training pairwhich gives high accuracy

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