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inference and contradictory analysis for binary neural networks

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Science China(Technological Sciences) 1996年第1期39卷 11-16页

作者：郭宝龙郭雷 Department of Detection Technology Xidian UniversityXi'an 710071China Department of Automatic Control Northwest Polytechnic UniversityXi'an 710072China

A weak-inference theory and a contradictory analysis for binary neural networks (BNNs).are *** 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 *** from general constraint satisfaction networks,the solutions found by BNNs may contain inferior contradiction but not superior contradiction.

关键词： neural networks inference contradictory analysis.

An Initial Residual Stress inference Method by Incorporating Monitoring Data and Mechanism Model

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Chinese Journal of Mechanical Engineering 2022年第5期35卷 47-65页

作者： Shuguo Wang Yingguang Li Changqing Liu Zhiwei Zhao Nanjing University of Aeronautics and Astronautics Nanjing 210016China

Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality *** inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation *** to the principle error of existing residual stress detection methods,there are still challenges in practical *** at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this *** data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism ***,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism *** results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

关键词： Initial residual stress inference Monitoring data Mechanism model Policy gradient

Adaptive network fuzzy inference system based navigation controller for mobile robotAdaptive network fuzzy inference system based navigation controller for mobile robot

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Frontiers of Information Technology & Electronic Engineering 2019年第2期20卷 141-151页

作者： Panati SUBBASH Kil To CHONG Department of Electronics and Information Engineering Chonbuk National University Advanced Electronics and Information Research Center Chonbuk National University

Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system(ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.

关键词： Adaptive network fuzzy inference system Additive white Gaussian noise Autonomous navigation Mobile robot

Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

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International Journal of Minerals,Metallurgy and Materials 2019年第5期26卷 538-546页

作者： Jalil Pazhoohan Hossein Beiki Morteza EsfANDyari Department of Chemical Engineering Quchan University of Technology Department of chemical engineering University of Bojnord

The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.

关键词： flotation tailings leaching copper environments adaptive neural fuzzy inference system

A Novel Method of Gene Regulatory Network Structure inference from Gene Knock-Out Expression Data

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Tsinghua Science and Technology 2019年第4期24卷 446-454页

作者： Xiang Chen Min Li Ruiqing Zheng Siyu Zhao Jianxin Wang Fang-Xiang Wu Yaohang Li the School of Computer Science and Engineering Central South University the Department of Mechanical Engineering and Division of Biomedical Engineering University of Saskatchewan the Department of Computer Science Old Dominion University

Inferring Gene Regulatory Networks(GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information(PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theorybased network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

关键词： gene regulatory networks network inference path consistency algorithm

On the Foundations of Statistical Mechanics: Ergodicity, Many Degrees of Freedom and inference

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Communications in Theoretical Physics 2014年第10期61卷 469-475页

作者： Sergio Chibbaro Lamberto Rondoni Angelo Vulpiani Sorbonne Universités UPMC Univ Paris 06CNRSUMR7190Institut Jean Le Rond d’AlembertF-75005 ParisFrance Kavli Institute for Theoretical Physics China CAS Dip.Scienze Matematiche Politecnico di TorinoC.Duca degli Abruzzi 24TorinoItaly INFN Sezione di TorinoVia P.Giuria 1TorinoItaly Dipartimento di Fisica Università "La Sapienza" RomaItaly ISC-CNR Piazzale A.Moro 2 – 00185 RomaItaly

We review the main aspects of the foundations of statistical mechanics. In particular we explain why many degrees of freedom are necessary, while chaos(in the sense of positive Lyapunov exponents) is only marginally r... 详细信息

关键词： ergodic hypothesis chaos emergent properties statical inference

Knowledge Representation and Semantic inference of Process Based on Ontology and Semantic Web Rule Language

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第1期34卷 72-80页

作者： Zhu Haihua Li Jing Wang Yingcong College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert *** application mode of ontology and semantic technology on the process parameters recommendation are mainly ***,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are ***,the inference method about process based on ontology technology and the SWRL rule is *** construction method of process ontology base and the writing criterion of SWRL rule are described ***,the results of inference are *** mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

关键词： ontology semantic web rule language (SWRL) process plan knowledge semantic inference

The Exact inference of Beta Process and Beta Bernoulli Process From Finite Observations

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Computer Modeling in Engineering & Sciences 2019年第10期121卷 49-82页

作者： Yang Cheng Dehua Li Wenbin Jiang School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China.

Beta Process is a typical nonparametric Bayesian *** the Beta Bernoulli Process provides a Bayesian nonparametric prior for models involving collections of binary valued *** previous studies considered the Beta Process inference problem by giving the Stick-Breaking sampling *** paper focuses on analyzing the form of precise probability distribution based on a Stick-Breaking approach,that is,the joint probability distribution is derived from any finite number of observable samples:It not only determines the probability distribution function of the Beta Process with finite observation(represented as a group of number between[0,1]),but also gives the distribution function of the Beta Bernoulli Process with the same finite dimension(represented as a matrix with element value of 0 or,1)by using this distribution as a prior.

关键词： Beta process joint distribution beta Bernoulli process exact inference

State Sampling Dependence of Hopfield Network inference

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Communications in Theoretical Physics 2012年第1期57卷 169-172页

作者： HUANG Hal-Ping Key Laboratory of Frontiers in Theoretical Physics Institute of Theoretical PhysicsChinese Academy of SciencesBeijing 100190China Department of Physics Hong Kong University of Science and TechnologyHong KongChina

The fully connected Hopfield network is inferred based on observed magnetizations and pairwise *** present the system in the glassy phase with low temperature and high memory *** find that the inference error is very sensitive to the form of state *** a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled ***,the error can be greatly reduced if the data is collected with state *** result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.

关键词： inference Hopfield network spin glass