1 research outputs found
Optimizing method for Neural Network based on Genetic Random Weight Change Learning Algorithm
Random weight change (RWC) algorithm is extremely component and robust for
the hardware implementation of neural networks. RWC and Genetic algorithm (GA)
are well known methodologies used for optimizing and learning the neural
network (NN). Individually, each of these two algorithms has its strength and
weakness along with separate objectives. However, recently, researchers combine
these two algorithms for better learning and optimization of NN. In this paper,
we proposed a methodology by combining the RWC and GA, namely Genetic Random
Weight Change (GRWC), as well as demonstrate a seminal way to reduce the
complexity of the neural network by removing weak weights of GRWC. In contrast
to RWC and GA, GRWC contains an effective optimization procedure which is
worthy at exploring a large and complex space in intellectual strategies
influenced by the GA/RWC synergy. The learning behavior of the proposed
algorithm was tested on MNIST dataset and it was able to prove its performance.Comment: 2 pages, Published in ICROS 2017 conference, South Kore