A CORRELATION SIGNIFICANCE LEARNING SCHEME FOR AUTO-ASSOCIATIVE MEMORIES

1995 ◽  
Vol 06 (04) ◽  
pp. 455-462
Author(s):  
DONQ-LIANG LEE ◽  
WEN-JUNE WANG
Keyword(s):  
Neural Network ◽  
Exponential Type ◽  
Correlation Matrix ◽  
Gradient Descent ◽  
Error Function ◽  
Connection Matrix ◽  
Product Rule ◽  
Associative Memories ◽  
Outer Product ◽  
Weighted Correlation

A new concept called correlation significance for expanding the attraction regions around all the stored vectors (attractors) of an asynchronous auto-associative memory is introduced. Since the well known outer product rule adopts equally-weighted correlation matrix for the neuron connections, the attraction region around each attractor is not maximized. In order to maximize these attraction regions, we devise a rule that all the correlations between two different components of two different stored patterns should be unequally weighted. By this formalism, the connection matrix T of the asynchronous neural network is designed by using the gradient descent approach. Additionally, an exponential type error function is constructed such that the number of successfully stored vectors can be directly examined during the entire learning process. Finally, computer simulations demonstrate the efficiency and capability of this scheme.

Decisional Autonomy of Approach and Landing Phase for Civil Aviation Aircraft using Dual Fuzzy Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 936-939 ◽  
Author(s):  
Kai Jun Xu
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Fuzzy Neural Network ◽  
Error Function ◽  
Civil Aviation ◽  
Learning Rates ◽  
The Neural Network ◽  
Automatic Landing ◽  
Fuzzy Neural

This paper presents the dual fuzzy neural network, designed the decisional autonomy flight controller for civil aviation aircraft in approach and landing phase. Real-time learning method was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. Conventional automatic landing system (ALS) can provide a smooth landing, which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this paper is to investigate the use of dual fuzzy neural network in ALS and to make that system more intelligent.

Learning in the Recurrent Random Neural Network

1993 ◽  
Vol 5 (1) ◽  
pp. 154-164 ◽  
Author(s):  
Erol Gelenbe
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Random Network ◽  
Error Function ◽  
Network Models ◽  
Neuron Network ◽  
Neural Network Models ◽  
Random Neural Network ◽  
Analytical Properties

The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function. The analytical properties of the model lead to a "backpropagation" type algorithm that requires the solution of a system of n linear and n nonlinear equations each time the n-neuron network "learns" a new input-output pair.

PATTERN STORAGE AND HOPFIELD NEURAL ASSOCIATIVE MEMORY WITH HIDDEN STRUCTURE

1992 ◽  
Vol 03 (03) ◽  
pp. 315-322 ◽  
Author(s):  
SHAOHUA TAN ◽  
JIANBIN HAO ◽  
JOOS VANDEWALLE
Keyword(s):  
Associative Memory ◽  
Memory Performance ◽  
Memory Storage ◽  
Design Approach ◽  
Data Driven ◽  
Product Rule ◽  
Associative Memories ◽  
Fundamental Issue ◽  
Outer Product

This paper is concerned with the formulation of neural associative memories. Centered around the fundamental issue of the memory storage, we examine the deficiencies associated with the standard Hopfield net. To overcome the problems, we pursue a data-driven design approach by modifying the configuration of the Hopfield net to allow hidden structures. As important results, we show how the well-known sum-of-outer product rule can be utilized to explore the freedom provided by the hidden structures leading to the desired memory performance.

LEARNING OF THE HOPFIELD ASSOCIATIVE MEMORY BY GLOBAL MINIMIZATION

1994 ◽  
Vol 08 (01) ◽  
pp. 373-390
Author(s):  
TAO WANG
Keyword(s):  
Gradient Descent ◽  
Storage Capacity ◽  
Learning Algorithm ◽  
Basin Of Attraction ◽  
Basins Of Attraction ◽  
Connection Matrix ◽  
Global Minimization ◽  
Associative Memories ◽  
Optimal Learning ◽  
The Cost

In the paper, a learning algorithm for Hopfield associative memories (HAMs) is presented. According to the cost function that measures the goodness of the HAM, we determine the connection matrix using a global minimization, solved by a gradient descent rule. This optimal learning method can guarantee the storage of all training patterns with basins of attraction that are as large as possible. We also study the storage capacity of the HAM, the asymptotic stability of each training pattern and its basin of attraction. A large number of computer simulations have been conducted to show its performance.

scholarly journals Estimation of Navigation Mark Floating Based on Fractional-Order Gradient Descent with Momentum for RBF Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qionglin Fang
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Gradient Descent ◽  
Rbf Neural Network ◽  
Error Function ◽  
Initial Position ◽  
Descent Method ◽  
Local Optimum ◽  
Gradient Descent Method ◽  
The Neural Network

To address the difficulty of estimating the drift of the navigation marks, a fractional-order gradient with the momentum RBF neural network (FOGDM-RBF) is designed. The convergence is proved, and it is used to estimate the drifting trajectory of the navigation marks with different geographical locations. First, the weight of the neural network is set. The navigation mark’s meteorological, hydrological, and initial position data are taken as the input of the neural network. The neural network is trained and used to estimate the mark’s position. The navigation mark’s position is taken at a later time as the output of the neural network. The difference between the later position and the estimated position obtained from the neural network is the error function of the neural network. The influence of sea conditions and months are analyzed. The experimental results and error analysis show that FOGDM-RBF is better than other algorithms at trajectory estimation and interpolation, has better accuracy and generalization, and does not easily fall into the local optimum. It is effective at accelerating convergence speed and improving the performance of a gradient descent method.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

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