neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found...neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found dominant in in-terpeptides.Weights of each kind of residues, obtained by this work,suggestedsome different scales for the hydrophobicity of the residue.These will be helpful in understanding polypeptide structure and protein folding.展开更多
In this paper, by using Liapunov functional, some sufficient conditions are obtained for the stability of the equilibrium of a neural network model with delay of the type u ′ i(t)=-b iu i(t)+∑nj=1T ij f ...In this paper, by using Liapunov functional, some sufficient conditions are obtained for the stability of the equilibrium of a neural network model with delay of the type u ′ i(t)=-b iu i(t)+∑nj=1T ij f j(μ ju j(t-τ j))+c i, τ j≥0, i=1,2,…,n.展开更多
The global asymptotic stability for Hopfield neural networks with time delay was investigated, A theorem and two corollaries were obtained, in which the boundedness and differentiability of f(j) on R in some articles ...The global asymptotic stability for Hopfield neural networks with time delay was investigated, A theorem and two corollaries were obtained, in which the boundedness and differentiability of f(j) on R in some articles were deleted. Some sufficient conditions for the existence of global asymptotic stable equilibrium of the networks in this paper are better than the sufficient conditions in quoted articles.展开更多
In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learnin...In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learning rules and two algorithms are presented. Simulation results indicate that such network has satisfactory generalization properties near the sample points. Since this kind of neural nets can be easily operated and implemented, it is appropriate to make further research concerning the theory and applications of GCNN.展开更多
The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematic...The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.展开更多
he pattern recognition method and artificial neural network method to predict the composition of epilayer of GaInAsSb by MOCVD. It is concluded that a neural network with the composition of the vapor phase and growth ...he pattern recognition method and artificial neural network method to predict the composition of epilayer of GaInAsSb by MOCVD. It is concluded that a neural network with the composition of the vapor phase and growth temperature as training data can predict the composition of the epilayers. Satisfactory pattern recognition and artificial neural network classification results were obtained by using four technical parameters as characteristic features and the epilayers composition as classification criteria.展开更多
Sufficient conditions are obtained for the existence, uniqueness and stability of T-periodic solutions far the Hopfield neural network equations with delay [GRAPHICS]
A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model...A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model the systems with multi-valued mapping such as hysteresis.In order to handle this problem,a novel hysteretic operator is proposed to extract the dynamic property of the hysteresis.Moreover,it can construct an expanded input space to transform the multi-valued mapping of hysteresis into one-to-one mapping.Then neural networks can directly be used to approximate the behavior of dynamic hysteresis.Finally,the experimental results are presented to illustrate the potential of the proposed modeling method.展开更多
Based on wave digital filter(WDF) principles, this paper presents a digital model of cellular neural networks(CNNs). The model can precisely simulate the dynamic behavior of CNNs.
On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial n...On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial neural network is trained by an improved BP algorithm.The results state that the fault sonic signals can be identified by trained network system precisely.展开更多
In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in ...In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.展开更多
文摘neural network model based on backbone propagation was applied to Learn-ing and predicting the interaction between antiparallelly interactive peptide seg-ments in proteins.Hydrophobic properties pf residues were found dominant in in-terpeptides.Weights of each kind of residues, obtained by this work,suggestedsome different scales for the hydrophobicity of the residue.These will be helpful in understanding polypeptide structure and protein folding.
文摘In this paper, by using Liapunov functional, some sufficient conditions are obtained for the stability of the equilibrium of a neural network model with delay of the type u ′ i(t)=-b iu i(t)+∑nj=1T ij f j(μ ju j(t-τ j))+c i, τ j≥0, i=1,2,…,n.
文摘The global asymptotic stability for Hopfield neural networks with time delay was investigated, A theorem and two corollaries were obtained, in which the boundedness and differentiability of f(j) on R in some articles were deleted. Some sufficient conditions for the existence of global asymptotic stable equilibrium of the networks in this paper are better than the sufficient conditions in quoted articles.
文摘In this paper a novel class of neural networks called generalized congruence neural networks (GCNN) is proposed. All neurons in the neural networks are activated in the form of congruence. The architectures, learning rules and two algorithms are presented. Simulation results indicate that such network has satisfactory generalization properties near the sample points. Since this kind of neural nets can be easily operated and implemented, it is appropriate to make further research concerning the theory and applications of GCNN.
基金Supported by the National Natural Science Foundation of China(No.11372309,61304017)Science and Technology Development Plan Key Project of Jilin Province(No.20150204074GX)the Science and Technology Special Fund Project of Provincial Academy Cooperation(No.2017SYHZ00024)
文摘The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.
文摘he pattern recognition method and artificial neural network method to predict the composition of epilayer of GaInAsSb by MOCVD. It is concluded that a neural network with the composition of the vapor phase and growth temperature as training data can predict the composition of the epilayers. Satisfactory pattern recognition and artificial neural network classification results were obtained by using four technical parameters as characteristic features and the epilayers composition as classification criteria.
文摘Sufficient conditions are obtained for the existence, uniqueness and stability of T-periodic solutions far the Hopfield neural network equations with delay [GRAPHICS]
基金supported by the National Natural Science Foundation of China(No.61273184)the Program for Changjiang Scholars and Innovative Research Team in University(No.IRT13097)the Natural Science Foundation of Zhejiang Province(Nos.LY15F030022, LY13E050025,LZ15F030005)
文摘A dynamic hysteresis model based on neural networks is proposed for piezoelectric actuator.Neural network has been widely applied to pattern recognition and system identification.However,it is unable to directly model the systems with multi-valued mapping such as hysteresis.In order to handle this problem,a novel hysteretic operator is proposed to extract the dynamic property of the hysteresis.Moreover,it can construct an expanded input space to transform the multi-valued mapping of hysteresis into one-to-one mapping.Then neural networks can directly be used to approximate the behavior of dynamic hysteresis.Finally,the experimental results are presented to illustrate the potential of the proposed modeling method.
文摘Based on wave digital filter(WDF) principles, this paper presents a digital model of cellular neural networks(CNNs). The model can precisely simulate the dynamic behavior of CNNs.
文摘On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial neural network is trained by an improved BP algorithm.The results state that the fault sonic signals can be identified by trained network system precisely.
文摘In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.
