Nt1310 Unit 1 Data Analysis

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The problem I am going to work on is #68 on page 539 . The

The following steps are used to design the back propagation neural network algorithm for the proposed research work. The first step is to set the input, output data sets. The second step is to set the number of hidden layer and output activation functions. The third step is to set the training functions and training parameters, finally run the network.

as vanishing gradient problem ??. Since network parameters of an RNN are shared over time, at any time step error

The training is divided into two phases: learning phase and testing phase. In the learning phase, an iterative which updated the synoptic weights is formed upon the error BP (Back Propagation) algorithm. In the testing phase the number of input and output parameters as well as the cases number influenced the neural network,whereas the trained results is then compared to the target to make a decision about the continuing of the iteration or the obtained results is concluded. The common ANN structure for the three architectures is (3X3), which means three neurons in the input layer and three neurons in the hidden layer. The training of each ANN architecture designs are shown in the following: fig.3, fig.4 and fig.5,

A linear formula idea will be used and the decision variables will be labeled as follow:

Following that, the expected values for decision nodes 6 and 7 should also be calculated. The following results were obtained:

The paper has been divided into 5 parts. Part II presents the summary of various neural networks used. Part III consists of the selection of data and error analysis. Results of simulation are discussed in Part IV. Part V concludes the paper and presents ideas on future work.

In this paper some of the notations used are describe according to [9] as follow:

Navneet Gupta and Ravindra Pratap Narwariya used ANN to design a low pass (FIR) filter. The optimization of the network has been done using generalized regression neural network (GRNN) [6]. The proposed approach compared with the

FSW) and Artificial neural networks (ANN) are two well known approaches in their respective field. FSW is a well known method in material sciences and ANN in computer science. The aim is to combine these two approaches to get some important results.

In this work balckman and bartlett window is used. In this work three neural network algorithms i.e. FFDTD, FFBP and RBF have been used and result obtained by these algorithms have been compared and it found that RBF gives better result than other two algorithms i.e. FFDTD and FFBP.

In machine learning, systems are trained to infer patterns from observational data. A particularly simple type of pattern, a mapping between input and output, can be learnt through a process called supervised learning. A supervised-learning system is given training data consisting of example inputs and the corresponding outputs, and comes up with a model to explain those data (a process called function approximation). It does this by choosing from a class of model specified by the system’s designer. [Nature. ANN 4]

SVM classifiers worked the best in majority of papers. In this project, SVM classifier with linear kernel is used.

In [14] proposed a comparison of different approaches in initialization of neural network weights and the most of algorithms that were used in multilayer neural networks and they had been based on various levels of modification of random weight