Abstract :
Arti cial Neural Networks (ANNs) are applied to many complex real-world
problems, ranging from image recognition to autonomous robot control. However, to design
a neural network that can implement special task, it is necessary to select an appropriate
biological neuron model, meanwhile, good learning algorithm should be adopted to achieve
the expected goal. Neuroevolution is a form of machine learning that uses Evolutionary
Algorithms (EAs) to train ANNs. EAs, for the learning algorithm used by neural networks,
can provide alternative and complementary solution, which can avoid the frequently
happened issues of \getting stuck in local minimum" during the iteration process made
by gradient-based learning algorithms. In this paper, a method using Hybrid PSO-based
Learning Algorithm (HPLA) to evolve the connection weights and network parameters of
Binary-Weights Neural Network (BWNN) will be introduced. The extracted knowledge
from trained BWNN can then be used to construct high-speed shift-and-add based Color
Space Converter (CSC) hardware architecture. The experimental results in this research
also show that the performance of implemented hardware architecture is good at RGB
to YCbCr color space converting, and it also has the advantages of high-speed and lowcomplexity.
