What Is The Backpculation Of Forward Propagation And Backpropagation?

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

One of the biggest advantages of Neural Network is that it can actually learn from observing data sets. This way it uses a random function approximation tool, which helps to estimate the most efficient and ideal solution while defining all the computing functions and distributions. Neural Networks takes data samples instead of entire data sets to arrive to a solution, which saves a lot of time and money. Neural Networks are considered as simple mathematical models to enhance existing data analysis technology.

The major reason that the field of advanced neural networks was reborn in the 1980s was because the breakthrough in technology allowed researchers with the ability experiment new theories and methodologies on artificial neural networks at a critical level. Others included more advanced contributions to AI theory and design (adaptive resonance theory, the back-propagation learning algorithm) and the advancements of reinforcement learning in the field of neuroscience.

More accuracy can be achieved by refining our data and knowing the significance of different attributes in the data, or the role of training or transfer functions. The role of neural networks and its working is still a mystery and is gradually evolution

The idea of neural computing grew out of desire to capture pattern recognition capabilities of a biological brain. Neural network usually presented as system interconnected ‘’neuron’’ that can compute values from inputs by feeding

Multilayer Perceptron (MLP) is an artificial neural network that learns nonlinear function mappings. An MLP can be viewed as a logistic regression classifier where the input is first transformed using a learned non-linear transformation. This transformation projects the input data into space where it becomes linearly separable. This intermediate layer is referred to as a hidden layer. A single hidden layer is sufficient to make MLPs a universal approximator.

One of this AI technologies tools is the Artificial Neural Networks which work much like the human brain and have the ability to learn from training

Some of these models need input parameter from the user, thus are heuristically designed and hence performance might depend on characteristic of parameters and input image. Some of the new learning-based methods overcome above problems and give solutions to complex problems. It is for this reason that deep neural networks have recently seen an impressive comeback. CNN (Convolutional Neural Network) used in Deep learning for image restoration, works by averaging out the output of various trained network to the same input. Neural Networks have numerous application in several areas of image processing. It is used for classifying the image and the mathematical analysis of CNN operates feature extraction first and then give the results to trainable classifier. This model works by training the network to reconstruct high quality images from degraded or blurred input images. The model gives promising results from the learned set of denoisers and also can be used for low level applications to deliver high performance

1. Describe the 7 layer OSI model of communication layers? Discuss Each Layer in detail.

In behavior of an ANN depends in each of the weights and the input-output function (transfer function) that is selected for the units. This function usually located into one of three types[10]:

CNN. This will include a literature review of the most recent peer-reviewed papers in computer

According to the computational process, human brain and a machines that use deep learning techniques generally have major difference on three levels; (1) at the implementation level, human brain uses neurons as the basic units to process the signals while the computers use transistors as the basic logic gates for all operations on the data. (2) at the algorithmic level, brains use connections such as axons to link the functional components of the brains and activate the corresponding components for different tasks. However, computers use the symbolic representation of the running processes and execute them with the step by step symbolic machine computations. (3) at the computational level, the brains activate all the relevant cells to perform the tasks concurrently with distributed parallel structures while computers generally use operating systems to compute the tasks in a serial manner.

consist of three layers of nodes : input layer, hidden layer , and output layer . The input layer nodes denoted $x_i , i in mathbb{N}$ ,where each of one of them is connected to all nodes in the hidden layer $h_j , j in mathbb{N}$ via connection weight that. Also , each node in the hidden layer is connected to all nodes in the output layer $ y_k , k in mathbb{N}$ through different connection weights .

At the simplest level, neural networks are a new way of analyzing data. The revolutionary aspect of neural networks is their ability to learn and trace the complex patterns and trends in data. Neural networks are made up of neurons and behave like the human brain, and has the ability to apply knowledge from past experience to new problems. Neural networks acquire this knowledge by training on a set of data. After the network has been trained and validated, the model may be applied to data it has not seen previously for prediction, classification, time series analysis or data segmentation.

The network is tested for 10 patterns and the table 3.1 shows the comparison of results of CST and FFBP-ANN with 5 neurons for radius with the variation of the resonant frequency of the circular patch microstrip antenna computing MSE function with constant substrate height and dielectric constant.