FPGA Implementation of SVM for Nonlinear Systems Regression

FPGA Implementation of SVM for Nonlinear Systems Regression

This work resumes the previous implementations of Support Vector Machine for Classification and Regression and explicates the different methods and approaches adopted. Ever since the rarity of works in the field of nonlinear systems regression, an implementation of testing phase of SVM was proposed exploiting the parallelism and reconfigurability of FieldProgrammable Gate Arrays (FPGA) platform...

Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression

In this paper, we propose a new approach aiming to ameliorate the performances of the regularization networks (RN) method and speed up its computation time. A considerable rapidity in totaling calculation time and high performance were accomplished through conveying difficult calculation charges to FPGA. Using Xilinx System Generator, a successful HW/SW CoDesign was constructed to accelerate th...

Hardware Implementation of a Neural Network Controller with an MCU and an FPGA for Nonlinear Systems

This paper presents the hardware implementation of a neural network controller for a nonlinear system with a micro-controller unit (MCU) and a field programmable gate array (FPGA) chip. As an on-line learning algorithm of a neural network, the reference compensation technique has been implemented on an MCU, while PID controllers with other functions such as counters and PWM generators are imple...

FPGA-based Implementation of Signal Processing Systems

FPGA implementation of a nonlinear two dimensional fuzzy filter

Nonlinear filtering has found many practical applications in digital signal and image processing. The computation complexity of these filtering algorithms make them difficult for real-time hardware implementation. One of these nonlinear filters, which is based on fuzzy classification of each pixel to subgroups of its neighboring pixels, is considered here for hardware implementation. The criter...