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
This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey ...This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.展开更多
The authors developed a prototype of a warship maintenance system. The process started by defining the maintenance requirements of warship equipment. Next, a planning scheme was development for a maintenance network. ...The authors developed a prototype of a warship maintenance system. The process started by defining the maintenance requirements of warship equipment. Next, a planning scheme was development for a maintenance network. An optimization target for the plan and indexes for assessment were established. Based on the above work, a simulation model was proposed with two layers: a base and a workshop. Dispatching rules were then formulated for the simulation. Experimental results proved the validity of the model and the dispatching algorithm. It was found that the model can solve the capacity evaluation problem for maintenance systems and provides a scientific basis for decision-maker to make decisions regarding equipment maintenance.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.
文摘This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.
基金Supported by the National Natural Science Foundation of China under Grant No.60774036
文摘The authors developed a prototype of a warship maintenance system. The process started by defining the maintenance requirements of warship equipment. Next, a planning scheme was development for a maintenance network. An optimization target for the plan and indexes for assessment were established. Based on the above work, a simulation model was proposed with two layers: a base and a workshop. Dispatching rules were then formulated for the simulation. Experimental results proved the validity of the model and the dispatching algorithm. It was found that the model can solve the capacity evaluation problem for maintenance systems and provides a scientific basis for decision-maker to make decisions regarding equipment maintenance.
