Neural networks approach for solving the Maximal Constraint Satisfaction Problems

Ettaouil, Mohamed; Haddouch, Khalid; Hami, Youssef; Loqman, Chakir

doi:10.1109/sita.2013.6560794

Cited by 3 publications

(4 citation statements)

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“…CTRNNs have been used before to solve specific constraint satisfaction problems using a generalized energy function (Ettaouil et al, 2013 ; Haddouch et al, 2013 ). In that approach, an energy function is defined by taking into account the objective function and a function to penalize the violation of task-correlated constraints.…”

Section: Methodsmentioning

confidence: 99%

Self-Optimization in Continuous-Time Recurrent Neural Networks

Zarco

Froese

2018

Front. Robot. AI

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A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called self-modeling or self-optimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will spontaneously generalize over these typically suboptimal attractors and thereby also reinforce more optimal attractors-even if these better solutions are normally so hard to find that they have never been previously visited. Ideally, after sufficient self-optimization the most optimal attractor dominates the state space, and the network will converge on it from any initial condition. This technique has been applied to social networks, gene regulatory networks, and neural networks, but its application to less restricted neural controllers, as typically used in evolutionary robotics, has not yet been attempted. Here we show for the first time that the selfoptimization process can be implemented in a continuous-time recurrent neural network with asymmetrical connections. We discuss several open challenges that must still be addressed before this technique could be applied in actual robotic scenarios.

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Section: Methodsmentioning

confidence: 99%

Self-Optimization in Continuous-Time Recurrent Neural Networks

Zarco

Froese

2018

Front. Robot. AI

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“…The neural network approaches are the efficient approaches for solving different problems in different areas [2]- [4]- [9]- [1]- [19]. Moreover, Hopfield and Tank [10]- [11] presented the energy function approach in order to solve several optimization problems [2]- [6].…”

Section: The Proposed Model Solved By Continuous Hopfield Networkmentioning

confidence: 99%

“…According to the proposed model, which consists modeling the WCSP problem into a quadratic programming QP, this step of representation becomes easy and more general. Then, the continuous Hopfield network can be used to solve the weighted constraint satisfaction problem [1]- [18].…”

Section: The Proposed Model Solved By Continuous Hopfield Networkmentioning

confidence: 99%

“…We observe that, the weights and thresholds of the continuous Hopfield network depend on the parameters α , φ , β and γ . To solve the QP problem via the CHN, an appropriate setting of these parameters is needed [1]- [19]. The parameter-setting procedure is based on the partial derivatives of the generalized energy function:…”

Section: The Proposed Model Solved By Continuous Hopfield Networkmentioning

confidence: 99%

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Solving the Weighted Constraint Satisfaction Problems Via the Neural Network Approach

Haddouch¹,

Elmoutaoukil²,

Ettaouil³

2016

IJIMAI

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-A wide variety of real world optimization problems can be modelled as Weighted Constraint Satisfaction Problems (WCSPs). In this paper, we model this problem in terms of in original 0-1 quadratic programming subject to leaner constraints. View it performance, we use the continuous Hopfield network to solve the obtained model basing on original energy function. To validate our model, we solve several instance of benchmarking WCSP. In this regard, our approach recognizes the optimal solution of the said instances.

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