A weak-inference theory and a contradictory analysis for binary neural networks (BNNs).are presented.The analysis indicates that the essential reason why a neural network is changing its slates is the existence of sup...A weak-inference theory and a contradictory analysis for binary neural networks (BNNs).are presented.The analysis indicates that the essential reason why a neural network is changing its slates is the existence of superior contradiction inside the network,and that the process by which a neural network seeks a solution corresponds to eliminating the superior contradiction.Different from general constraint satisfaction networks,the solutions found by BNNs may contain inferior contradiction but not superior contradiction.展开更多
By comparison with constraint satisfaction networks, this paper presents an essential frame of the logical theory for continuous-state neural networks, and gives the quantitative analyzing method for contradiction. Th...By comparison with constraint satisfaction networks, this paper presents an essential frame of the logical theory for continuous-state neural networks, and gives the quantitative analyzing method for contradiction. The analysis indicates that the basic reason for the alternation of the logical states of the neurons is the existence of superior contradiction inside the networks. The dynamic process for a neural network to find a solution corresponds to eliminating the superior contradiction.展开更多
In this investigation, an approach using Coac-tive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to ...In this investigation, an approach using Coac-tive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the rela-tionships between the large measured factors, which can be possibly resolved with a human like decision-making process using Artificial Intelligence (AI) algorithms. CANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks and can deal with ambiguous data and learn from the past data by itself. The Multi Layer Percep-tron Neural Network (MLPNN), Probabilistic Neural Network (PNN) Principal Component Analysis (PCA), Support Vector Machine (SVM) and Self Organizing Map (SOM) were also tested and benchmarked for their展开更多
基金Project supported by the National Natural Science Foundation of China, and the National High-tech Project of China (863).
文摘A weak-inference theory and a contradictory analysis for binary neural networks (BNNs).are presented.The analysis indicates that the essential reason why a neural network is changing its slates is the existence of superior contradiction inside the network,and that the process by which a neural network seeks a solution corresponds to eliminating the superior contradiction.Different from general constraint satisfaction networks,the solutions found by BNNs may contain inferior contradiction but not superior contradiction.
基金Supported by the National Natural Science Foundation of Chinathe National Hightech Project of China(863)
文摘By comparison with constraint satisfaction networks, this paper presents an essential frame of the logical theory for continuous-state neural networks, and gives the quantitative analyzing method for contradiction. The analysis indicates that the basic reason for the alternation of the logical states of the neurons is the existence of superior contradiction inside the networks. The dynamic process for a neural network to find a solution corresponds to eliminating the superior contradiction.
文摘In this investigation, an approach using Coac-tive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the rela-tionships between the large measured factors, which can be possibly resolved with a human like decision-making process using Artificial Intelligence (AI) algorithms. CANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks and can deal with ambiguous data and learn from the past data by itself. The Multi Layer Percep-tron Neural Network (MLPNN), Probabilistic Neural Network (PNN) Principal Component Analysis (PCA), Support Vector Machine (SVM) and Self Organizing Map (SOM) were also tested and benchmarked for their
