The Configurable Digital Cellular Neural - Hopfield Network

2006 ◽  
Author(s):  
T. Zeffer ◽  
T. Hidvegi
Keyword(s):  
Hopfield Network ◽  
Digital Cellular

scholarly journals Single Neuronal Dynamical System in Self-Feedbacked Hopfield Networks and Its Application in Image Encryption

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 456
Author(s):  
Xitong Xu ◽  
Shengbo Chen
Keyword(s):  
Dynamical System ◽  
Image Encryption ◽  
Chaotic Dynamic ◽  
Security Analysis ◽  
High Sensitivity ◽  
Hopfield Network ◽  
Complex Structures ◽  
Hopfield Networks ◽  
Discrete Form ◽  
Chaotic Parameter

Image encryption is a confidential strategy to keep the information in digital images from being leaked. Due to excellent chaotic dynamic behavior, self-feedbacked Hopfield networks have been used to design image ciphers. However, Self-feedbacked Hopfield networks have complex structures, large computational amount and fixed parameters; these properties limit the application of them. In this paper, a single neuronal dynamical system in self-feedbacked Hopfield network is unveiled. The discrete form of single neuronal dynamical system is derived from a self-feedbacked Hopfield network. Chaotic performance evaluation indicates that the system has good complexity, high sensitivity, and a large chaotic parameter range. The system is also incorporated into a framework to improve its chaotic performance. The result shows the system is well adapted to this type of framework, which means that there is a lot of room for improvement in the system. To investigate its applications in image encryption, an image encryption scheme is then designed. Simulation results and security analysis indicate that the proposed scheme is highly resistant to various attacks and competitive with some exiting schemes.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

Efficient matching algorithm by a hybrid Hopfield network for object recognition

1992 ◽  
Author(s):  
Jung H. Kim ◽  
Sung H. Yoon ◽  
Yong H. Kim ◽  
Evi H. Park ◽  
Celestine A. Ntuen ◽  
...  
Keyword(s):  
Object Recognition ◽  
Hopfield Network ◽  
Matching Algorithm ◽  
Efficient Matching

EVALUATION OF TASK ALLOCATION IN MESH-BASED MULTICOMPUTERS USING GENETIC, NEURAL, AND TRADITIONAL ALGORITHMS

1994 ◽  
Vol 03 (01) ◽  
pp. 47-60
Author(s):  
R.A. McCONNELL ◽  
B.L. MENEZES
Keyword(s):  
Task Allocation ◽  
Bin Packing ◽  
Packing Problem ◽  
Allocation Problem ◽  
Hopfield Network ◽  
Processor Allocation ◽  
Classical Level ◽  
Bin Packing Problem ◽  
Residual Space ◽  
Computer Tasks

This article compares three techniques for allocating tasks in a mesh-based multi-computer. Tasks are expressed as rectangles of a certain width and height corresponding to the topology of processors desired. The task allocation problem, is thus a variant of the bin-packing problem, with one major difference: in the bin-packing problem one seeks to minimize the height of the bin, while here we seek to maximize the utilization of processors in a multicomputer. The three techniques compared are a classical level-by-level algorithm, a connectionist simulated annealing variant of the Hopfield network, and a genetic algorithm. An extension to the dynamic processor allocation problem is modeled by fixing some rectangles in place and packing the request rectangles in the residual space on the mesh; this corresponds to a pre-existing condition, i.e., some tasks have already been allocated to the Processor Mesh. Implementation and experimental results are presented.

ENERGY RELAXATION FOR HOPFIELD NETWORK WITH THE NEW LEARNING RULE

2009 ◽  
Author(s):  
Saratha Sathasivam ◽  
Abdul Halim Hakim ◽  
Pandian Vasant ◽  
Nader Barsoum
Keyword(s):  
Learning Rule ◽  
Energy Relaxation ◽  
Hopfield Network ◽  
New Learning
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