Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to ...Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.展开更多
The combinatorial optimization problem(COP),which aims to find the optimal solution in discrete space,is fundamental in various fields.Unfortunately,many COPs are NP-complete,and require much more time to solve as the...The combinatorial optimization problem(COP),which aims to find the optimal solution in discrete space,is fundamental in various fields.Unfortunately,many COPs are NP-complete,and require much more time to solve as the problem scale increases.Troubled by this,researchers may prefer fast methods even if they are not exact,so approximation algorithms,heuristic algorithms,and machine learning have been proposed.Some works proposed chaotic simulated annealing(CSA)based on the Hopfield neural network and did a good job.However,CSA is not something that current general-purpose processors can handle easily,and there is no special hardware for it.To efficiently perform CSA,we propose a software and hardware co-design.In software,we quantize the weight and output using appropriate bit widths,and then modify the calculations that are not suitable for hardware implementation.In hardware,we design a specialized processing-in-memory hardware architecture named COPPER based on the memristor.COPPER is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further acceleration.The results show that COPPER can perform CSA remarkably well in both speed and energy.展开更多
文摘Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model ? chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.
基金Project supported by the National Natural Science Foundation of China(Nos.61832020,62032001,92064006,and 62274036)the Beijing Academy of Artificial Intelligence(BAAI)of Chinathe 111 Project of China(No.B18001)。
文摘The combinatorial optimization problem(COP),which aims to find the optimal solution in discrete space,is fundamental in various fields.Unfortunately,many COPs are NP-complete,and require much more time to solve as the problem scale increases.Troubled by this,researchers may prefer fast methods even if they are not exact,so approximation algorithms,heuristic algorithms,and machine learning have been proposed.Some works proposed chaotic simulated annealing(CSA)based on the Hopfield neural network and did a good job.However,CSA is not something that current general-purpose processors can handle easily,and there is no special hardware for it.To efficiently perform CSA,we propose a software and hardware co-design.In software,we quantize the weight and output using appropriate bit widths,and then modify the calculations that are not suitable for hardware implementation.In hardware,we design a specialized processing-in-memory hardware architecture named COPPER based on the memristor.COPPER is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further acceleration.The results show that COPPER can perform CSA remarkably well in both speed and energy.
