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Associative Neural Network

Mr. Rishabh Singh Rathore

2019
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International Journal for Research in Applied Science and Engineering Technology
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A collaborative neural network (ASN) is a combination of feed-forward neural networks and a group of closest neighboring techniques. The network offered represents a correlation between the responses collected as the measure of distance between the cases analyzed for the nearest neighbor technology and provides a better prediction by the bias improvement of the neural network ensemble. A collaborative neural network has a memory that can be found with the training set. If new data becomes
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... data becomes available, the network improves further forecasting ability and can often provide a reasonable estimate of the unknown function without the need to stop the piece of neural network. I. INTRODUCTION The traditional synthetic feed-forward neural network (ANN) is a bleak vision. This means that after completing the training, all the information about the input pattern is stored in neural network weight and input data is not required, i.e. there is no clear storage of any presented instance in the system. On the contrary, the closest-to-neighbors (KNN) (for example, Dasherty, 1991), perjonwindow regressions (e.g., Hurdle, 1990), etc., represent the memory-based approach. These approaches keep the whole database of memory in memory and their predictions are based on some local projections of stored examples. Neural networks can be considered as a global model, while the other two approaches are generally considered to be local models (Lawrence et al., 1996) . For example, consider the problem of multiplexing function approximation, i.e. finding mapping RM => RN from given set of sample points. For simplicity, we assume that n = 1 A global model input provides a good estimate of the data space RM's global metric. However, if the work analyzed, F, is very complex, then there is no guarantee that all details of F, i.e., will be represented in its good structure. Thus, the global model can be insufficient because it does not mainly describe the entire state's place due to the high bias of the global model in certain areas of space. ANN variation can also contribute to poor performance of this method (Gemman et al., 1992). However, variation can be reduced by analyzing the large number of variation networks, i.e. using the artificial neural network ensemble (ANNE), and for example, taking a general average of all networks in the form of the last model. The problem of ANN's bias can not be easily addressed, for example, using such a large nervous network, such networks can fall into the local minimum and thus there may still be enough bias. Local models are based on some neighborhood relationships, and analyzing these methods are more relevant to the discovery of a good structure of analysis tasks, i.e. they can get less bias than the global model. However, while applying these methods, the difficult question is how to properly determine the neighborhood relations in the analysis area? Input data analyzed, especially in practical applications, there can be a large number of dimensions and for the final representation, the actual importance of each input parameter and contribution is generally not known. Example 1. Consider the example of the sign function y = sin (x) (1) With dimensions of 1 equals vector x. The training and test sets consisted of N = 100 and 1000 cases respectively, and the input values were evenly distributed over parallel (0, π). The KNN method was used Z (2) JNN K (X) where z (x) is the estimated value for case X, using the NK (E) Euclidean metric, the training set {xi} Ni = 1 is a collection of the closest neighbors of X between the input vectors x x, xi ||. Note that the memory of KNN was shown by the entire training set {xi} Ni = 1. Number K = 1 was selected to provide minimum leave-one-out error (LOO) for the training set. KNN calculated the basic mean square error, RMS = 0.016 for the test set. Similar results, RMS = 0.022 was calculated by a group of M = 100 ANN According to the Levenburg-Marquart Algorithm (press et al., 1994), trained with 2 hidden neurons (a hidden layer) trained. Input and output values were generalized for normal (0.1.0.9) intervals and the signogyd activation function was used for all neurons. In this and all the analyzes 50% of the cases were given opportunity and each neural network (TETCO et al., 1995) was used as a training set. The rest of the cases were initially used as a verification set Stopping Method (Bishop, 1995) . Thus, each neural network had its own training and verification set. To predict the test network, all the networks were used after learning a simple

doi:10.22214/ijraset.2019.4103
fatcat:ukiarvw2ynfydmg25vjkp7ofs4