Relevant bibliographies by topics / Hopfield algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hopfield algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Hopfield algorithm"

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Yu, Bin. "A Method of Hopfield Neural Network Based Nearest Neighbor Mode Solving Logistics Distribution." Applied Mechanics and Materials 263-266 (December 2012): 2054–57. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2054.

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To solve the problem of invalid and not optimal result for Hopfield neural network to logistics distribution, a nearest neighbor mode Hopfield neural network algorithm based on improved-loop is constructed. The solution of logistics distribution is initialized by nearest neighbor matrix, to seek optimized solution of logistics distribution with the energy function evolvement of Hopfield neural network. To verify this algorithm and the result of simulation, which is compared to other well-known algorithms, indicated that , nearest neighbor mode Hopfield neural network algorithm would avoid invalid result, and better than other well-known algorithm both in convergence rate and quality of optimization.
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Darintsev, O. V., and A. B. Migranov. "Modification of the hopfield neural network model for solving the task of optimal task allocation in a group of mobile robots." Teoriâ i sistemy upravleniâ, no. 2 (September 24, 2024): 169–82. http://dx.doi.org/10.31857/s0002338824020145.

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In the context of group interaction among mobile robots, there arises the challenge of task distribution within the group, considering the robots' characteristics and the working environment. This study aims to modify the Hopfield neural network and develop methodologies for its application in solving the task allocation problem for an arbitrary number of tasks within a group of mobile robots. To achieve this, the Hopfield neural network is represented as a graph. An algorithm is presented, demonstrating the transition from the initial problem to the Traveling Salesman Problem (TSP). The application of the Hopfield model to the task distribution problem in a group of robots is described, along with the development of an optimization function calculation algorithm. An assessment is conducted to evaluate the impact of neural network parameters on the quality and speed of solving the optimization problem. By comparing it with other heuristic methods (genetic and ant colony algorithms), the domains of application for the modified algorithm are determined.
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WU, CHUNG-HSIEN, JHING-FA WANG, CHAUG-CHING HUANG, and JAU-YIEN LEE. "SPEAKER-INDEPENDENT RECOGNITION OF ISOLATED WORDS USING CONCATENATED NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 05 (1991): 693–714. http://dx.doi.org/10.1142/s0218001491000417.

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A speaker-independent isolated word recognizer is proposed. It is obtained by concatenating a Bayesian neural network and a Hopfield time-alignment network. In this system, the Bayesian network outputs the a posteriori probability for each speech frame, and the Hopfield network is then concatenated for time warping. A proposed splitting Learning Vector Quantization (LVQ) algorithm derived from the LBG clustering algorithm and the Kohonen LVQ algorithm is first used to train the Bayesian network. The LVQ2 algorithm is subsequently adopted as a final refinement step. A continuous mixture of Gaussian densities for each frame and multi-templates for each word are employed to characterize each word pattern. Experimental evaluation of this system with four templates/word and five mixtures/frame, using 53 speakers (28 males, 25 females) and isolated words (10 digits and 30 city names) databases, gave average recognition accuracies of 97.3%, for the speaker-trained mode and 95.7% for the speaker-independent mode, respectively. Comparisons with K-means and DTW algorithms show that the integration of the splitting LVQ and LVQ2 algorithms makes this system well suited to speaker-independent isolated word recognition. A cookbook approach for the determination of parameters in the Hopfield time-alignment network is also described.
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Argollo de Menezes, M., and T. J. P. Penna. "Improving the Hopfield–Tank Approach for the Traveling Salesman Problem." International Journal of Modern Physics C 08, no. 05 (1997): 1095–102. http://dx.doi.org/10.1142/s0129183197000965.

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In this work we revisit the Hopfield–Tank algorithm for the traveling salesman problem (J. J. Hopfield and D. W. Tank, Biol. Cybern. 52, 141 (1985)) and report encouraging results, with a different dynamics, that makes the algorithm more efficient, finding better solutions in much less computational time.
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Priyatno, Gregorius Algananda Hartanto, and Rizki Dwi Romadhona. "Algoritma Hopfield dalam menentukan rute tercepat untuk pendistribusian telur kepada konsumen." Journal of Information System and Application Development 1, no. 2 (2023): 101–10. http://dx.doi.org/10.26905/jisad.v1i2.11066.

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ENJ Poultry Shop is a business that operates in the field of egg distribution. The main problem faced is determining delivery routes, which are still less effective. The focus of this research is optimization to determine the fastest route through implementing the Hopfield algorithm in the application. With the Hopfield algorithm, it is hoped that it can provide a solution for finding the shortest route to distribute eggs to consumers. This research is quantitative, where the analysis is carried out by comparing the results of manual calculations with the results of calculations using the application. Data collection was carried out at a number of locations, which were used as delivery destination addresses. The Hopfield algorithm is applied by determining the delivery point to be addressed, then measuring each delivery distance so that an estimate of the distance at each point is obtained. After obtaining all the data, it is continued with calculations using the Hopfield algorithm. Based on the research results, it can be concluded that the implementation of the application works better, resulting in the fastest distance and time in determining the egg delivery route. By optimizing distribution time, it is possible to increase marketing and provide consumer satisfaction.
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Muhammad Sidik, Siti Syatirah, Nur Ezlin Zamri, Mohd Shareduwan Mohd Kasihmuddin, Habibah A. Wahab, Yueling Guo, and Mohd Asyraf Mansor. "Non-Systematic Weighted Satisfiability in Discrete Hopfield Neural Network Using Binary Artificial Bee Colony Optimization." Mathematics 10, no. 7 (2022): 1129. http://dx.doi.org/10.3390/math10071129.

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Recently, new variants of non-systematic satisfiability logic were proposed to govern Discrete Hopfield Neural Network. This new variant of satisfiability logical rule will provide flexibility and enhance the diversity of the neuron states in the Discrete Hopfield Neural Network. However, there is no systematic method to control and optimize the logical structure of non-systematic satisfiability. Additionally, the role of negative literals was neglected, reducing the expressivity of the information that the logical structure holds. This study proposed an additional optimization layer of Discrete Hopfield Neural Network called the logic phase that controls the distribution of negative literals in the logical structure. Hence, a new variant of non-systematic satisfiability named Weighted Random 2 Satisfiability was formulated. Thus, a proposed searching technique called the binary Artificial Bee Colony algorithm will ensure the correct distribution of the negative literals. It is worth mentioning that the binary Artificial Bee Colony has flexible and less free parameters where the modifications tackled on the objective function. Specifically, this study utilizes a binary Artificial Bee Colony algorithm by modifying the updating rule equation by using not and (NAND) logic gate operator. The performance of the binary Artificial Bee Colony will be compared with other variants of binary Artificial Bee Colony algorithms of different logic gate operators and conventional binary algorithms such as the Particle Swarm Optimization, Exhaustive Search, and Genetic Algorithm. The experimental results and comparison show that the proposed algorithm is compatible in finding the correct logical structure according to the initiate ratio of negative literal.
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Xiao, Yao, Yashu Zhang, Xiangguang Dai, and Dongfang Yan. "Clustering Based on Continuous Hopfield Network." Mathematics 10, no. 6 (2022): 944. http://dx.doi.org/10.3390/math10060944.

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Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence of the proposed recurrent neural network theoretically. Moreover, clustering experiments demonstrate that the proposed clustering algorithm based on the recurrent neural network can achieve the better clustering performance than existing clustering algorithms.
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Wang, Xun, and Jie Rong. "The Computer Network Optimization Model Based on Neural Network Algorithm Research." Advanced Materials Research 798-799 (September 2013): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.545.

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The speed of development of the computer network is an urgent need to comprehensively improve and optimize the overall performance of the network. Neural network algorithm has a massively parallel processing and distributed information storage, Hopfield neural network showed a unique advantage in the associative memory and optimization based on the neural network algorithm for computer network optimization model of Hopfield neural network theory and reality computer network, modern optimization methods, it is combined.
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Benedetti, Marco, Enrico Ventura, Enzo Marinari, Giancarlo Ruocco, and Francesco Zamponi. "Supervised perceptron learning vs unsupervised Hebbian unlearning: Approaching optimal memory retrieval in Hopfield-like networks." Journal of Chemical Physics 156, no. 10 (2022): 104107. http://dx.doi.org/10.1063/5.0084219.

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The Hebbian unlearning algorithm, i.e., an unsupervised local procedure used to improve the retrieval properties in Hopfield-like neural networks, is numerically compared to a supervised algorithm to train a linear symmetric perceptron. We analyze the stability of the stored memories: basins of attraction obtained by the Hebbian unlearning technique are found to be comparable in size to those obtained in the symmetric perceptron, while the two algorithms are found to converge in the same region of Gardner’s space of interactions, having followed similar learning paths. A geometric interpretation of Hebbian unlearning is proposed to explain its optimal performances. Because the Hopfield model is also a prototypical model of the disordered magnetic system, it might be possible to translate our results to other models of interest for memory storage in materials.
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Someetheram, Vikneswari, Muhammad Fadhil Marsani, Mohd Shareduwan Mohd Kasihmuddin, et al. "Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm." Mathematics 10, no. 24 (2022): 4734. http://dx.doi.org/10.3390/math10244734.

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Real life logical rule is not always satisfiable in nature due to the redundant variable that represents the logical formulation. Thus, the intelligence system must be optimally governed to ensure the system can behave according to non-satisfiable structure that finds practical applications particularly in knowledge discovery tasks. In this paper, we a propose non-satisfiability logical rule that combines two sub-logical rules, namely Maximum 2 Satisfiability and Random 2 Satisfiability, that play a vital role in creating explainable artificial intelligence. Interestingly, the combination will result in the negative logical outcome where the cost function of the proposed logic is always more than zero. The proposed logical rule is implemented into Discrete Hopfield Neural Network by computing the cost function associated with each variable in Random 2 Satisfiability. Since the proposed logical rule is difficult to be optimized during training phase of DHNN, Election Algorithm is implemented to find consistent interpretation that minimizes the cost function of the proposed logical rule. Election Algorithm has become the most popular optimization metaheuristic technique for resolving constraint optimization problems. The fundamental concepts of Election Algorithm are taken from socio-political phenomena which use new and efficient processes to produce the best outcome. The behavior of Random Maximum 2 Satisfiability in Discrete Hopfield Neural Network is investigated based on several performance metrics. The performance is compared between existing conventional methods with Genetic Algorithm and Election Algorithm. The results demonstrate that the proposed Random Maximum 2 Satisfiability can become the symbolic instruction in Discrete Hopfield Neural Network where Election Algorithm has performed as an effective training process of Discrete Hopfield Neural Network compared to Genetic Algorithm and Exhaustive Search.
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Dissertations / Theses on the topic "Hopfield algorithm"

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Halabian, Faezeh. "An Enhanced Learning for Restricted Hopfield Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42271.

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This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architecture/components of the model, we propose a modified variant of SPSA which in conjunction with back-propagation over time result in a training algorithm with an enhanced convergence for RHN. The trained network is also shown to achieve a better memory recall in the presence of noisy/distorted input. We perform several experiments, using various datasets, to verify the convergence of the training sub-routine, evaluate the impact of different parameters of the model, and compare the performance of the trained RHN in recreating distorted input patterns compared to conventional RBM and Hopfield network and other training methods.
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Balavoine, Aurèle. "Implementation of the locally competitive algorithm on a field programmable analog array." Thesis, Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37255.

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Sparse approximation is an important class of optimization problem in signal and image processing applications. This thesis presents an analog solution to this problem, based on the Locally Competitive Algorithm (LCA). A Hopfield-Network-like analog system, operating on sub-threshold currents is proposed as a solution. The results of the circuit components' implementation on the RASP2.8a chip, a Field Programmable Analog Array, are presented.
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Shapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.

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Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.
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Chen, Hsinchun, and Tobun Dorbin Ng. "An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/105241.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify â convergentâ concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network).
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Stískal, Břetislav. "Návrh algoritmů pro neuronové sítě řídicí síťový prvek." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217527.

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This diploma thesis is devided into theoretic and practice parts. Theoretic part contains basic information about history and development of Artificial Neural Networks (ANN) from last century till present. Prove of the theoretic section is discussed in the practice part, for example learning, training each types of topology of artificial neural networks on some specifics works. Simulation of this networks and then describing results. Aim of thesis is simulation of the active networks element controlling by artificial neural networks. It means learning, training and simulation of designed neural network. This section contains algorithm of ports switching by address with Hopfield's networks, which used solution of typical Trade Salesman Problem (TSP). Next point is to sketch problems with optimalization and their solutions. Hopfield's topology is compared with Recurrent topology of neural networks (Elman's and Layer Recurrent's topology) their main differents, their advantages and disadvantages and supposed their solution of optimalization in controlling of network's switch. From thesis experience is introduced solution with controll function of ANN in active networks elements in the future.
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Hsu, Cheng-Ming, and 許宸銘. "VT-CVQ using fuzzy Hopfield neural network clustering algorithm." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/88339590888618419087.

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碩士<br>國立中央大學<br>電機工程學系<br>85<br>In image compression techniques, vector quantization (VQ) has been found to be a powerful approach. It provides many attractive feature when high compression ratio is desired. However, from the initial study of VQ for image coding, we can find several problems, such as high computational complexity and edge degradation. In this thesis, we take advantages of some schemes about classification instead of traditional full-search VQ to solve the problem mention-above. We also modified the new transformation, called vector transform (VT), and use fuzzy Hopfield neural network (FHNN) as clustering algorithm to replace traditional method, DCT and LBG algorithm. In CVQ structure, we extract four coefficients of the modified 2-dimencional VT that possess edge integrity and constitute coefficients to be feature vector for every subimage. The edge- oriented classifier based on FHNN algorithm are then executed on these feature vectors to cluster the training data. After classification, we design a subcodebook for each class using LBG algorithm. Simulation results indicate that the better visual and slightly improvement in distortion are obtained in our proposed techniques when comparing the existent DCT-CVQ.
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Xu, Chen-Ming, and 許宸銘. "VT-CVQ using fuzzy Hopfield neural network clustering algorithm." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/04227223188504660688.

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David, Andrew J. H. "Optical implementation of the Hopfield algorithm using cross-correlations of unipolar data." 1989. http://catalog.hathitrust.org/api/volumes/oclc/20476440.html.

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Thesis (M.S.)--University of Wisconsin--Madison, 1989.<br>Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 48-49).
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Huang, Chin-Chung, and 黃清忠. "A Methodology for the Integration of Hopfield Network and Genetic Algorithm Schemes for Graph Matching Problems." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/07832368643089770958.

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博士<br>國立中山大學<br>機械與機電工程學系研究所<br>93<br>Object recognition is of much interest in recent industrial automation. Although a variety of approaches have been proposed to tackle the recognition problem, some cases such as overlapping objects, articulated objects, and low-resolution images, are still not easy for the existing schemes. Coping with these more complex images has remained a challenging task in the field. This dissertation, aiming to recognize objects from such images, proposes a new integrated method. For images with overlapping or articulated objects, graph matching methods are often used, seeing them as solving a combinatorial optimization problem. Both Hopfield network and the genetic algorithm are decent tools for the combinatorial optimization problems. Unfortunately, they both have intolerable drawbacks. The Hopfield network is sensitive to its initial state and stops at a local minimum if it is not properly given. The GA, on the other hand, only finds a near-global solution, and it is time-consuming for large-scale tasks. This dissertation proposes to combine these two methods, while eliminating their bad and keeping their good, to solve some complex recognition problems. Before the integration, some arrangements are required. For instance, specialized 2-D GA operators are used to accelerate the convergence. Also, the “seeds” of the solution of the GA is extracted as the initial state of the Hopfield network. By doing so the efficiency of the system is greatly improved. Additionally, several fine-tuning post matching algorithms are also needed. In order to solve the homomorphic graph matching problem, i.e., multiple occurrences in a single scene image, the Hopfield network has to repeat itself until the stopping criteria are met. The method can not only be used to obtain the homomorphic mapping between the model and the scene graphs, but it can also be applied to articulated object recognition. Here we do not need to know in advance if the model is really an articulated object. The proposed method has been applied to measure some kinematic properties, such as the positions of the joints, relative linear and angular displacements, of some simple machines. The subject about articulated object recognition has rarely been mentioned in the literature, particularly under affine transformations. Another unique application of the proposed method is also included in the dissertation. It is about using low-resolution images, where the contour of an object is easily affected by noise. To increase the performance, we use the hexagonal grid in dealing with such low-resolution images. A hexagonal FFT simulation is first presented to pre-process the hexagonal images for recognition. A feature vector matching scheme and a similarity matching scheme are also devised to recognize simpler images with only isolated objects. For complex low-resolution images with occluded objects, the integrated method has to be tailored to go with the hexagonal grid. The low-resolution, hexagonal version of the integrated scheme has also been shown to be suitable and robust.
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Lee, Chou-Yuan, and 李秋緣. "Hybrid Search Algorithms of Hopfield Neural Networks and Ant Colony Systems for Traveling Salesman Problem." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/66678451069412954794.

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博士<br>國立臺灣科技大學<br>電機工程系<br>96<br>In this dissertation, hybrid search algorithms of combining Hopfield neural networks and ant colony systems for traveling salesman problem are proposed. The Traveling salesman problem (TSP) is a well-known NP-complete combinatorial optimization problem and is often employed as a benchmark for the evaluation of search efficiency for various search algorithms. The algorithm of Ant colony systems (ACS) is a class of algorithms by using artificial ants with the capability of mimicking the behavior of real ants to find the optimal path. Hopfield neural networks (HNN) are a parallel input and output networks structure, which can also be to solve optimization problems. In this study, we intend to study possible combinations of Hopfield neural networks and ant colony systems. Various algorithms are proposed in this dissertation. In the first approach, HNN is used to find a plausibly good solution (locally optimum), which is then used in ACS as the currently best tour for the offline pheromone trail update. The idea is to deposit additional pheromone to ACS to enhance the search efficiency. The idea is very simple, but very effective. The second approach is to employ HNN as a local search mechanism in ACS. The idea is to consider HNN as a greedy algorithm for finding local optimum. The proposed algorithms also demonstrated excellent search performance. It can be found that even though HNN needs only a small fraction of execution time, while using as the local search mechanism in each iteration for ACS, HNN still becomes a computational burden in the hybrid search algorithm. As a result, the search efficiency of the second hybrid search algorithm is worse than that of the first one. Experimental results of solving the traveling salesman problem are reported to justify our claims. Furthermore, an algorithm incorporating genetic algorithms and Hopfield neural networks with ant colony optimization is then proposed to deal with the HNN initial problem. In this approach, Genetic Algorithm (GA) is employed to provide a feasible candidate for HNN so that the search can become efficient. From our experiments, it is clearly evident that the proposed algorithm indeed can significantly improve the search efficiency for the search algorithm without GA. Finally, when the city number increases, the weight matrix of HNN will become extremely large. We also propose a simple way of dealing with this problem. The experiments conducted indeed demonstrate the effectiveness of the proposed approach.
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Books on the topic "Hopfield algorithm"

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Cierniak, Robert. Nowe algorytmy rekonstrukcji obrazu z projekcji z zastosowaniem sieci neuronowych typu Hopfielda. Wydawn. Politechniki Częstochowskiej, 2006.

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Book chapters on the topic "Hopfield algorithm"

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Uykan, Zekeriya, Murat Can Ganiz, and Çağla Şahinli. "Discrete-Time Hopfield Neural Network Based Text Clustering Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34475-6_66.

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Yu, Ensheng, and Xicheng Wang. "A Subgraph Isomorphism Algorithm Based on Hopfield Neural Network." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_73.

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Dong, Guang-jun, Yong-sheng Zhang, and Chao-jie Zhu. "Remote Sensing Image Classification Algorithm Based on Hopfield Neural Network." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_49.

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Weinfeld, Michel. "A Fully Digital Integrated CMOS Hopfield Network Including the Learning Algorithm." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-1619-0_15.

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Masuyama, Naoki, and Chu Kiong Loo. "An Iterative Incremental Learning Algorithm for Complex-Valued Hopfield Associative Memory." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46681-1_51.

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Wang, Nan, Lin Wang, Xueqiang Gu, Jing Chen, and Lincheng Shen. "Hopfield Neural Network Guided Evolutionary Algorithm for Aircraft Penetration Path Planning." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12990-2_27.

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Grossi, Giuliano, and Roberto Posenato. "A Distributed Algorithm for Max Independent Set Problem Based on Hopfield Networks." In Neural Nets. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45808-5_6.

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Chen, Xiaoming, Zheng Tang, Xinshun Xu, Songsong Li, Guangpu Xia, and Jiahai Wang. "An Algorithm Based on Hopfield Network Learning for Minimum Vertex Cover Problem." In Advances in Neural Networks – ISNN 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_72.

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Wu, Hsin-Lung, Jui-Sheng Chang, and Jen-Chun Chang. "A New Approximation Algorithm for the d-dimensional Knapsack Problem Based on Hopfield Networks." In Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03745-1_5.

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Pullanatt, Anuranj, and A. Anitha. "VANET Hybrid Routing Protocol Featuring Perpetual Hopfield Network and Enhanced K-Means Clustering Algorithm." In Communication and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_43.

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Conference papers on the topic "Hopfield algorithm"

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Lin, Ci, Tet Yeap, and Iluju Kiringa. "Subspace Rotation Algorithm for Training Restricted Hopfield Network." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00110.

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Yanping Gao, Changhui Deng, and Guoxing Jiang. "Improvement of Hopfield neural network algorithm." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486096.

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Lei, Yan, and Wang Meng. "Hopfield Neural Network based Stereo Matching Algorithm." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326480.

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Del Balio, R., E. Tarantino, and R. Vaccaro. "A Parallel Algorithm For Asynchronous Hopfield Neural Networks." In IEEE International Workshop on Emerging Technologies and Factory Automation,. IEEE, 1992. http://dx.doi.org/10.1109/etfa.1992.683336.

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Hassoun, M. H., and A. M. Youssef. "New Recording Algorithm For Hopfield Model Associative Memories." In 1988 Los Angeles Symposium--O-E/LASE '88, edited by Ravindra A. Athale and Joel Davis. SPIE, 1988. http://dx.doi.org/10.1117/12.944101.

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Liu, Chen, Chenghai Li, Fangzheng Zhao, Zijie Zhao, and Hongyi Qiao. "Image encryption algorithm based on modified SC-Hopfield." In Eleventh International Conference on Digital Image Processing, edited by Xudong Jiang and Jenq-Neng Hwang. SPIE, 2019. http://dx.doi.org/10.1117/12.2540196.

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Chen, Ju, Chengfeng Zheng, Yuan Gao, and Yueling Guo. "Genetic algorithm in hopfield neural network with probabilistic 2 satisfiability." In CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning. ACM, 2023. http://dx.doi.org/10.1145/3590003.3590024.

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Ma, Xiaohu, Licai Zhong, and Xueyan Chen. "Application of Hopfield Neural Network Algorithm in Mathematical Modeling." In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2023. http://dx.doi.org/10.1109/csnt57126.2023.10134711.

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Yang Ming and Ze Min Liu. "Dual routing algorithm base on Hopfield in ATM networks." In Proceedings of APCC/OECC'99 - 5th Asia Pacific Conference on Communications/4th Optoelectronics and Communications Conference. IEEE, 1999. http://dx.doi.org/10.1109/apcc.1999.824496.

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Gao, Weixin, Nan Tang, and Xiangyang Mu. "A Distribution Network Reconfiguration Algorithm Based on Hopfield Neural Network." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.147.

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Reports on the topic "Hopfield algorithm"

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Valdez, Luis, Miltos Alamaniotis, and Alexander Heifetz. Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1894587.

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