Study on Neural Network Integration Method Based on Morphological Associative Memory Framework

Abstract

In traditional neural network integration, people adopt Boosting, Bagging and other methods to integrate traditional neural networks. The integration is complex, time-consuming and laborious, difficult to popularize and apply. This paper is not a continuation of this method, but another integration which is called by us morphological neural network integration (MNNI) or morphological associative memory integration (MAMI). These networks used in MAMI are a network family, with 10 family members, unified in the morphological associative memory framework. Various morphological associative memory networks can be directly used as individual networks to learn and work separately, and then synthesize to draw conclusions. The results of some experiments show that this method is not only feasible in theory, but also effective in practice. It can avoid the complexity of traditional integration method, make the integration structure simple and clear, easy to operate, save time, and therefore is a method of neural network integration with research and application value. The contribution of this paper lies in that: (1) it proposed the concept and method of MNNI and, (2) verified the effectiveness of MNNI through experiments and, (3) it has the characteristics of simplicity, saving time and labor and cost, with a good application prospect and, (4) thus promoting the development of morphological neural networks in theory and practice.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

“…With the emerge of deep neural networks (DNNs) [ [9] , [10] , [11] , [12] , [13] , [14] ], especially convolutional neural networks (CNNs), they leverage multi-level layer neural networks for representational learning and are widely used for image classification [ 15 , 16 ], object detection [ 17 , 18 ] and semantic segmentation [ 19 ]. Naturally, DNNs are very good at detecting COVID-19 [ [20] , [21] , [22] , [23] , [24] , [25] ].…”

Discrete-Time Hopfield Neural Network: Convergence Theorem: Perturbation Analysis

“…In this section, we consider the dynamics of HAM based on 𝑊 ̅ when its elements are subjected to interesting perturbations [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. We consider some interesting cases: CASE (A): All the elements of the symmetric matrix, 𝑊 ̅ are perturbed by a common value i.e.…”

Discrete time Hopfield neural network: convergence theorem: perturbation analysis

“…In this section, we consider the dynamics of HAM based on 𝑊 ̅ when its elements are subjected to interesting perturbations [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. We consider some interesting cases: CASE (A): All the elements of the symmetric matrix, 𝑊 ̅ are perturbed by a common value i.e.…”