Mutation Hopfield neural network and its applications

Hu, Laihong; Sun, Fuchun; Xu, Huan; Liu, Huaping; Zhang, Xuejun

doi:10.1016/j.ins.2010.08.007

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…In this case, the MHNN only requires a fragment of correct synaptic weight to retrieve the optimal final state during the learning phase. A similar perturbation strategy was utilized by Hu et al [22] in solving the max-cut problem. On the other hand, the EDA in the MHNN creates minor neuron oscillations and retrieves the state independently, although the network trained the suboptimal synaptic weight.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, the ideal mutation rate was chosen to be 0.01 to effectively investigate the impact of a mutation in the HNN. The choice of mutation rate has good agreement with [22]. A non-common parameter such as T was utilized in the BHNN and MFTHNN as the simulated annealing effect takes place in both models.…”

Section: Simulationmentioning

confidence: 99%

“…However, the usage of genetic algorithm during the retrieval phase of the HNN is considered inefficient due to the complexity of several operators, such as crossover and mutation. These two operators require more generations/iterations before the apparent improvement can take place [22]. Several studies indicate [48][49][50] that GAs entail a higher complexity, leading to premature convergence.…”

Section: Mutation Hopfield Neural Networkmentioning

confidence: 99%

“…The act of perturbation for the neuron state will generate the new starting point for the HNN and reduce the possible local minimum solution. Hu et al [22] proposed the mutation hopfield neural network (MHNN) to solve max-cut and aircraft landing schedule (ALS) problems. In this work, the proposed model utilized the EDA to explore other solution spaces that fulfil the given cost function.…”

Section: Introductionmentioning

confidence: 99%

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Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

et al. 2019

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The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k-satisfiability (kSAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.

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Section: Resultsmentioning

confidence: 99%

Section: Simulationmentioning

confidence: 99%

Section: Mutation Hopfield Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

et al. 2019

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“…Moreover, it would be worth exploring other effective algorithms to ensure the neuron in DHNN always converges to the global minimum energy. For instance, implementation of the Mutation operator [42] and memristor [43] were reported to increase the search space of the DHNN. Finally, the robust DHNN-RANMAX2SATEA has good potential to become good forecasting model for various real-life modeling that is random in nature such as flood modeling, seismic modeling, and tsunami modeling.…”

Section: Discussionmentioning

confidence: 99%

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

et al. 2022

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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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Drift analysis of mutation operations for biogeography-based optimization

Guo

Wang

et al. 2014

Soft Comput

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Mutation Hopfield neural network and its applications

Cited by 12 publications

References 30 publications

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

Random Maximum 2 Satisfiability Logic in Discrete Hopfield Neural Network Incorporating Improved Election Algorithm

Drift analysis of mutation operations for biogeography-based optimization

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