Novel Complex-Valued Neural Network for Dynamic Complex-Valued Matrix Inversion

2016 ◽  
Vol 20 (1) ◽  
pp. 132-138 ◽  
Author(s):  
Bolin Liao ◽  
◽  
Lin Xiao ◽  
Jie Jin ◽  
Lei Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hardware Implementation ◽  
Matrix Inversion ◽  
Matrix Inverse ◽  
Novel Complex ◽  
Simulation Results ◽  
Complex Valued ◽  
Gradient Neural Network

Static matrix inverse solving has been studied for many years. In this paper, we aim at solving a dynamic complex-valued matrix inverse. Specifically, based on the artful combination of a conventional gradient neural network and the recently-proposed Zhang neural network, a novel complex-valued neural network model is presented and investigated for computing the dynamic complex-valued matrix inverse in real time. A hardware implementation structure is also offered. Moreover, both theoretical analysis and simulation results substantiate the effectiveness and advantages of the proposed recurrent neural network model for dynamic complex-valued matrix inversion.

Predicting Stock Market Price Using Neural Network Model

2018 ◽  
Vol 9 (3) ◽  
pp. 84-94 ◽  
Author(s):  
Naliniprava Tripathy
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Model ◽  
Neural Network Model ◽  
Market Price ◽  
Efficient Market Hypothesis ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Daily Movement ◽  
Indian Stock Market

The present article predicts the movement of daily Indian stock market (S&P CNX Nifty) price by using Feedforward Neural Network Model over a period of eight years from January 1st 2008 to April 8th 2016. The prediction accuracy of the model is accessed by normalized mean square error (NMSE) and sign correctness percentage (SCP) measure. The study indicates that the predicted output is very close to actual data since the normalized error of one-day lag is 0.02. The analysis further shows that 60 percent accuracy found in the prediction of the direction of daily movement of Indian stock market price after the financial crises period 2008. The study indicates that the predictive power of the feedforward neural network models reasonably influenced by one-day lag stock market price. Hence, the validity of an efficient market hypothesis does not hold in practice in the Indian stock market. This article is quite useful to the investors, professional traders and regulators for understanding the effectiveness of Indian stock market to take appropriate investment decision in the stock market.

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