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Global Optimization Using Meta-Controlled Boltzmann Machine

Shamshul Bahar Yaakob, Junzo Watada

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Abstract

In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hop field network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hop field networks or Boltzmann machines.

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What this paper is about

In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hop field network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hop field networks or Boltzmann machines.

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Available abstract

In this study, a new artificial neuron network model called the meta-controlled Boltzmann machine is introduced. The meta-controlled Boltzmann machine model includes the McCulloch-Pitts model, the Hop field network, and also the Boltzmann machine. The proposed method are applied both diffusion processes and simulated annealing. The convergence proof of the proposed method is shows in this paper. Meta-controlled Boltzmann machine show an ability to solve combinatorial optimization problems better than either Hop field networks or Boltzmann machines.

Key concepts: Boltzmann machine, Boltzmann constant, Restricted Boltzmann machine, Computer science, Simulated annealing, Lattice Boltzmann methods, Convergence (economics), Boltzmann equation

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