20-09-2013, 03:02 PM
FPGA Implementation of Intelligent Systems for Virus Image Recognitions
Aim of the Research
Particle Swarm Optimization-feedforward Neural Network (PSONN) is proposed to enhance the learning process of ANN in term of convergence rate and classification and recognition accuracy
A Hardware Design of ANN platform (HDANN) is proposed to evolve the architecture of ANN circuits using FPGA-spartan3 board (XSA-3S1000 Board). The HDANN design platform creates ANN design files using WebPACKTM ISE 10.1, and converted into device-dependent programming files for eventual downloading into an FPGA device by using GXSLOAD program from the XSTOOLS programs.
Intelligent Systems
1- Artificial Neural Networks.
2- Genetic Algorithms.
3- Fuzzy Logic.
4- Swarm Intelligent Systems.
Artificial Neural Networks
An Artificial Neural Network (ANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the mammalian brain processes information. The key element of the ANN paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements that are analogous to neurons and are tied together with weighted connections that are analogous to synapses.
One of the most important neural tasks is pattern classification. In the following, it will be assumed that input patterns to be classified consist of elements that are continuous valued real numbers, that represents measurements of features selected to be useful for distinguishing between classes. The purpose of a pattern classifier is to partition the multidimensional pattern space into decision regions to indicate to which class any input belongs .
What Is Swarm?
Swarms are systems that consist of many identical individuals that are organized and coordinated by principles of decentralized control, indirect communication, and self-organization.
Conclusions
Using FPGA technology to implement digital artificial neurons in hardware, the HDANN platform has demonstrated the ability to design the ANNs. HDANN platform has the ease of re-implementation due to the parameterized modules as well as the state of the art for the chosen FPGA platform. It possesses the speed of hardware while retaining the flexibility of the software implementation due to the reprogramming ability of FPGA.
The proposed system is a general purpose ANN which is useful for many applications, and it is a tool that can be used to assist ANN researchers explore complex architecture ANNs using advanced neuron module. It has the potential to create ANN circuitry for AI applications.