Aim To propose a modelling method for flexible manipulators. Methods The improved algorithm and structure of the ANN (artificial neural networks) were used. All of the data used in the process of modelling came from e...Aim To propose a modelling method for flexible manipulators. Methods The improved algorithm and structure of the ANN (artificial neural networks) were used. All of the data used in the process of modelling came from experiments based on a very flexible link which was fixed on a FANUC Robot S-Model 300 in our lab.Results and Conclusion The theoretical analysis and experiment results showed that this modelling scheme is more suitable for flexible systems with characteristics of fast changing dynamics, and also it can be more accurate than others and is more convenient for real-time use.展开更多
The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid ...The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.展开更多
In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user req...In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.展开更多
文摘Aim To propose a modelling method for flexible manipulators. Methods The improved algorithm and structure of the ANN (artificial neural networks) were used. All of the data used in the process of modelling came from experiments based on a very flexible link which was fixed on a FANUC Robot S-Model 300 in our lab.Results and Conclusion The theoretical analysis and experiment results showed that this modelling scheme is more suitable for flexible systems with characteristics of fast changing dynamics, and also it can be more accurate than others and is more convenient for real-time use.
文摘The effects of cyanidation conditions on gold dissolution were studied by artificial neural network （ANN） modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P50 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients （R2） of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.9981, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively It is verified that the predicted values of ANN coincide well with the experimental results.
基金National Natural Science Foundation of China (No. 70631003)
文摘In agent-based automated negotiation research area,a key problem is how to make software agent more adaptable to represent user preferences or suggestions,so that agent can take further proposals that reflect user requirements to implement ecommerce activities like automated transactions.The difficulty lies in the uncertainty of user preferences that include uncertain description and contents,non-linear and dynamic variability.In this paper,fuzzy language was used to describe the uncertainty and combine with multiple classified artificial neural networks(ANNs) for self-adaptive learning of user preferences.The refinement learning results of various negotiation contracts' satisfaction degrees in the extent of fuzzy classification can be achieved.Compared to unclassified computation,the experimental results illustrate that the learning ability and effectiveness of agents have been improved.
