scholarly journals An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hui Chu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Risk Management ◽  
Human Brain ◽  
Associative Memory ◽  
Financial Management ◽  
Financial Risk ◽  
Control Measures ◽  
Time Lags ◽  
Time Varying

As a human brain-like computational model that can reflect the cognitive function of the brain, the problem of dynamic analysis of associative memory neural networks has attracted the attention of scholars. This paper combines associative memory neural networks with enterprise financial management risks, studies the synchronization control and stability analysis problems of unidirectional associative memory-like human brain amnestic neural networks with perturbation and mixed time-varying time lags, proposes a bidirectional associative memory-like brain stochastic amnestic neural network model with mixed time-varying time lags, designs a discrete-time sampling control strategy based on the model, and studies various types of recent financial risks. Based on the early warning research, based on the associative memory neural network method, we propose to reconstruct the risk categories, including improving the enterprise risk management system, enhancing the awareness of financial risk management from top to bottom, and strengthening the core competitiveness of the enterprise itself and control measures for financing, investment, operation, and cash flow risks.

Exponential lag function projective synchronization of memristor-based multidirectional associative memory neural networks via hybrid control

2018 ◽  
Vol 32 (09) ◽  
pp. 1850116 ◽  
Author(s):  
Manman Yuan ◽  
Weiping Wang ◽  
Xiong Luo ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
...  
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Time Delays ◽  
Hybrid Control ◽  
Projective Synchronization ◽  
Mixed Delays ◽  
Time Varying ◽  
Function Projective Synchronization ◽  
Different Types ◽  
Discrete Delays

This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

scholarly journals Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wei Feng ◽  
Simon X. Yang ◽  
Haixia Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Associative Memory ◽  
Robust Stability ◽  
Neutral Type ◽  
Time Varying ◽  
Bidirectional Associative Memory ◽  
Numerical Examples ◽  
Network Parameters ◽  
Robust Stability Analysis

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Bioinspired Associative Memories

2011 ◽  
pp. 248-255
Author(s):  
Roberto A. Vazquez ◽  
Humberto Sossa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Vision System ◽  
Special Kind ◽  
Input Pattern ◽  
3D Object Recognition ◽  
Infant Vision ◽  
Output Pattern ◽  
Full Power

An associative memory AM is a special kind of neural network that allows recalling one output pattern given an input pattern as a key that might be altered by some kind of noise (additive, subtractive or mixed). Most of these models have several constraints that limit their applicability in complex problems such as face recognition (FR) and 3D object recognition (3DOR). Despite of the power of these approaches, they cannot reach their full power without applying new mechanisms based on current and future study of biological neural networks. In this direction, we would like to present a brief summary concerning a new associative model based on some neurobiological aspects of human brain. In addition, we would like to describe how this dynamic associative memory (DAM), combined with some aspects of infant vision system, could be applied to solve some of the most important problems of pattern recognition: FR and 3DOR.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

Sign in / Sign up

Export Citation Format

Share Document