Implementation of Back Propagation Algorithm in Verilog Neelmani Chhedaiya neelmanichhedaiya@gmail.com SSGI Bhilai (C.G) Prof. Vishal Moyal vishalmoyal@gmail.com ABSTRACT In this paper, a design method of neural networks based on Verilog HDL hardware description language, implementation is proposed. A design of a general neuron for topologies using back propagation algorithm is described. The sigmoid nonlinear activation function is also used. The neuron is then used in the design and implementation of a neural network using Xilinx Spartan-3e FPGA. The simulation results obtained with Xilinx ISE 9.2i software. The backpropagation algorithm is one of the most useful algorithms of ANN training. In this paper, we present the neuron impleme- ntation for the in topologies that are suitable for this algorithm. The neuron is then used in a multilayer neural network. For the implementation, Verilog HDL language is used. Verilog HDL is a hardware description language which simplifies the development of complex systems because it is possible to model and simulate a digital system form a high level of abstraction and with important facilities for modular design. The purpose of this work is to suggest and analyze several neuron implementations, show a way for the integration and control of the neurons within a neural network, and describe a way to implement a simple feedforward neural network trained by BP algorithm using Xilinx 9.2i. Key words: Artificial Neural Network, Backpropagation, Verilog HDL. INTRODUCTION A Neural Network is a powerful data- modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways:  A neural network acquires knowledge through learning.  A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights. Following are the advantage of neural network- 1. An ability to learn how to do tasks based on the data given for training or initial experience. 2. An ANN can create its own organization or representation of the information it receives during learning time. 3. ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability. 4. Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage. Learning in neural network- Learning is a process by which the free parameters of a neural network are adapted through a process of stimulation by the environment in which the network is embedded. The type of learning is determined by the manner in which the parameter changes take place. All learning methods used for neural networks can be classified into two major categories: Supervised learning which incorporates an external teacher, so that each output unit is told what its desired response to input signals ought to be. During the learning process global information may be required. Paradigms of Neelmani Chhedaiya ,Int.J.Computer Technology & Applications,Vol 3 (1),340-343 IJCTA | JAN-FEB 2012 Available online@www.ijcta.com 340 ISSN:2229-6093