A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts： first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural n...A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts： first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural network are used to train the standardized samples. The proposed model was applied to assess the water quality of 16 sections in 9 rivers in the Shaoguan area in 2005. The evaluation result was compared with that of the RBF neural network method and the reported results in the Shaoguan area in 2005. It indicated that the performance of the proposed fuzzy neural network model is practically feasible in the application of water quality assessment and its operation is simple.展开更多
A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients c...A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients can be estimated by a linear algorithm. Thewavelet neural network holds some advantages superior to other typesof neural networks. First, its network structure is easy to specifybased on its theoretical analysis and intuition. Secondly, networktraining does not rely on stochastic gradient type techniques andavoids the problem of poor convergence or undesirable local minima.展开更多
Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and art...Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.展开更多
Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by th...Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.展开更多
Phenotypic plasticity is nearly universal among organisms, and evidence indicates that plasticity can exhibit additive genetic variation and respond to selection. These findings have important implications for our und...Phenotypic plasticity is nearly universal among organisms, and evidence indicates that plasticity can exhibit additive genetic variation and respond to selection. These findings have important implications for our understanding of how plasticity may be constrained and how its mechanistic structure may affect its evolution. Many life history trade-offs may be conceptua- lized as plastic traits, with individuals varying in their position along trade-off axes due to genetic differences, developmental plasticity, or short-term plasticity occurring throughout an individual＇s lifetime. Behavioral plasticity is key to understanding when organisms are likely to encounter trade-offs, whether those trade-offs can be mitigated, and how the tradc-offs affect the ecology and evolution of populations. In this review, we discuss hormonal and neural mechanisms that may influence how plastic behavioral traits are expressed and evolve. We also outline a classification of life history trade-offs and their mechanistic bases and discuss the currencies most likely to mediate each category of trade-off and bow they are tied to the mechanisms by which animals express their behaviors.展开更多
The brain of the domestic pig(Sus scrofa domesticus)has drawn considerable attention due to its high similarities to that of humans.However,the cellular compositions of the pig brain(PB)remain elusive.Here we investig...The brain of the domestic pig(Sus scrofa domesticus)has drawn considerable attention due to its high similarities to that of humans.However,the cellular compositions of the pig brain(PB)remain elusive.Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB(frontal lobe,parietal lobe,temporal lobe,occipital lobe,and hypothalamus)and identified 21 cell subpopulations.The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution.Furthermore,we identified cell types and molecular pathways closely associated with neurological disorders,bridging the gap between gene mutations and pathogenesis.We reported,to our knowledge,the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species,providing extensive resources for future research regarding neural science,evolutionary developmental biology,and regenerative medicine.展开更多
基金Supported by the National Key Research Program of China (No. 2003CCA00200)the Open Research Foundation of State KeyLab of Water Resources and Hydropower Engineering Science(No.2005C012).
文摘A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts： first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural network are used to train the standardized samples. The proposed model was applied to assess the water quality of 16 sections in 9 rivers in the Shaoguan area in 2005. The evaluation result was compared with that of the RBF neural network method and the reported results in the Shaoguan area in 2005. It indicated that the performance of the proposed fuzzy neural network model is practically feasible in the application of water quality assessment and its operation is simple.
基金Supported by the Eu Information Technologies Programme Project(No. 22416) and National High Tech R&D Project(863/Computer Integrated Manufacture System AA413130) of China.
文摘A type of wavelet neural network, in which the scale function isadopted only, is proposed in this paper for non-linear dynamicprocess modelling. Its network size is decreased significantly andthe weight coefficients can be estimated by a linear algorithm. Thewavelet neural network holds some advantages superior to other typesof neural networks. First, its network structure is easy to specifybased on its theoretical analysis and intuition. Secondly, networktraining does not rely on stochastic gradient type techniques andavoids the problem of poor convergence or undesirable local minima.
基金Supported by 863 Program of China(2002AA2Z4291) Henan Innovation Project for University Prominent Research Talents(2005KYCX015)Henan Project for University Prominent Talents
文摘Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.
文摘Six main influencing factors： slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the 13P neural network to predict mountain mining subsidence.
文摘Phenotypic plasticity is nearly universal among organisms, and evidence indicates that plasticity can exhibit additive genetic variation and respond to selection. These findings have important implications for our understanding of how plasticity may be constrained and how its mechanistic structure may affect its evolution. Many life history trade-offs may be conceptua- lized as plastic traits, with individuals varying in their position along trade-off axes due to genetic differences, developmental plasticity, or short-term plasticity occurring throughout an individual＇s lifetime. Behavioral plasticity is key to understanding when organisms are likely to encounter trade-offs, whether those trade-offs can be mitigated, and how the tradc-offs affect the ecology and evolution of populations. In this review, we discuss hormonal and neural mechanisms that may influence how plastic behavioral traits are expressed and evolve. We also outline a classification of life history trade-offs and their mechanistic bases and discuss the currencies most likely to mediate each category of trade-off and bow they are tied to the mechanisms by which animals express their behaviors.
基金the China Postdoctoral Science Foundation(2017M622795)the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20180507183628543)the Fundamental Research Funds for the Central Universities(2662018PY025 and 2662017PY105)。
文摘The brain of the domestic pig(Sus scrofa domesticus)has drawn considerable attention due to its high similarities to that of humans.However,the cellular compositions of the pig brain(PB)remain elusive.Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB(frontal lobe,parietal lobe,temporal lobe,occipital lobe,and hypothalamus)and identified 21 cell subpopulations.The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution.Furthermore,we identified cell types and molecular pathways closely associated with neurological disorders,bridging the gap between gene mutations and pathogenesis.We reported,to our knowledge,the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species,providing extensive resources for future research regarding neural science,evolutionary developmental biology,and regenerative medicine.
