水泥回转窑热工控制模型研究及其仿真

水泥回转窑是建材工业发展的方向，我国是水泥生产大国，而国内回转窑与发达国家相差甚大，尤其在热工控制方面。由于水泥回转窑具有时变、分布参数和非线性特性，是一个典型的复杂过程，因而水泥回转窑控制系统是一个很有意义且困难的研究方向，本论文在借鉴国内外同类研究的基础上，提出了模糊专家系统控制模型，进行了深入地研究，并且对该模型进行了计算机仿真，希望通过这项研究，提高我国在水泥回转窑先进智能技术的控制水平。主要研究内容有：对水泥回转窑的热工过程进行了详细分析，对其不同控制方法进行全面的综述，对水泥回转窑实现控制的人工智能方法进行了全面的综述，并介绍了国内外的研究现状；研究了对水泥回转窑控制的模糊控制模型、专家系统设计方法，以及利用模糊控制与专家系统相结合的方法对水泥回转窑进行安全而有效控制的方法：研究了专家系统的实时性问题，提出了静态排列专家系统的推理时间模型、优化排列专家系统的时间估计模型与排列准则；利用计算机仿真方法，实现对水泥回转窑这种复杂而昂贵系统控制进行实验研究，以较低的代价实现对其分析。本论文的主研究成果如下：1. 详细研究了水泥回转窑的技术发展与结构演化过程，分析了水泥回转窑的热工过程以及影响水泥生产的各种因素，总结了影响水泥生产质量的主要因素与次要因素，确定了控制水泥回转窑的主要并且可测量的过程参数。2. 用推理全成方法研究模糊控制模型，实现从模糊的角度研究水泥回转窑的控制：从专家 系统角度研究水泥回转窑的控制问题，并提取了有关的专家系统控制规则；在模糊控制与专家系统的基础上，将水泥回转窑的模糊控制与专家系统相结合，利用层次化的控制器结构，底层为模糊控制器，顶层为专家系统，实现了水泥回转窑的安全与有效控制。3. 从定量的角度研究了专家系统的推理时间问题，给出了三种相应的时间估计模型，这不仅可以分析水泥回转窑系统中的专家系统的实时性，而且也可以分析一般专家系统的推理时间和问题。4. 本文提出的计算机仿真工具，为三组数据分别进行计算机仿真，以此研究水泥回转窑控制策略的性能以及对其动态过程进行分析，为水泥回转窑这样的复杂且昂贵的控制系统研究提供有效的手段。

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

missing data imputation: a fuzzy k-means clustering algorithm over sliding window

Tianjin University of Technology

Pipeline Corrosion Analyzed by Fuzzy Reliability Methods

Fuzzy reliability methods are used to study the corrosion of pipelines. Three methods are used. They consist of using fracture failure modes, failure assessment diagram (FAD) and residual strength for establishing fuzzy reliability. Calculations are made by application of JC, improved GA-JC and Mente-carlo methods. Examples for oilfield injecting water pipeline show the residual strength well agree with field data. Mente-carlo methods appear to yield results that have better agreement with field data.

EXPERT-SYSTEM FOR ELUCIDATION OF STRUCTURES OF ORGANIC-COMPOUNDS

An expert system for the elucidation of the structures of organic compounds-ESESOC-II has been designed. It is composed of three parts: spectroscopic data analysis, structure generator, and evaluation of the candidate structures. The heart of ESESOC is the structure generator, as an integral part, which accepts the specific types of information, e.g. molecular formulae, substructure constraints, and produces an exhaustive and irredundant list of candidate structures. The scheme for the structural generation is given, in which the depth-first search strategy is used to fill the bonding adjacency matrix (BAM) and a new method is introduced to remove the duplicates.

Prediction of marine sediment corrosion by fuzzy clustering analysis

Along with the development of marine industries, especially marine petroleum exploitation, more and more pipelines are buried in the marine sediment. It is necessary and useful to know the corrosion environment and corrosiveness of marine sediment. In this paper, field corrosion environmental factors were investigated in Liaodong Bay marine sediment containing sulfate-reducing bacteria (SRB) and corrosion rate of steel in the partly sediment specimens were determined by the transplanting burying method. Based on the data, the fuzzy clustering analysis (FCA) was applied to evaluate and predict the corrosiveness of marine sediment. On that basis, the influence factors of corrosion damage were discussed.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

河流-三角洲储层自动对比微相自动识别原理方法和软件系统

Because of the complexity and particularity, especially the result is more depend on the expert' s experience, the calculate method which is based on the simplicity mathematical model can hardly have any effective role in the oilfield .The coalescent method of artificial intelligence and signal manage in the correlation of reservoir use log curve has been put forward.in this paper. Following the principle of "controlled by classification and correlation by deposit gyration ". The system of correlation has been setup, which can identify "standard layer" first by the improved method of gray connection system, and then on the basis of identified "standard layer", interpret the fault, and last identify the layer in the reservoir. A effective method of "the consistent character of a reservoir "has been adopt to solved the puzzle of interpret the fault. On the basis of sedimentary theory and the quantity analysis of log curve shape of different type microfacies, a serial of different type micofacies' s models has been build that use eight optimized parameters, five of eight rationed parameters being used to describe microfacies with log curve, the distribution area of every parameters for the microfacies has been give. Because the classical math can only be used in the areas that principles are very clearly, not be fit for the description of geology character, so The fuzzy math integrate judgment has been adopt in the using log curve to determine microfacies; the accordance ration is 85 percent. A set of software has been programmed which is on the system of Windows. the software has the integration function of data process, auto-contrast reservoir layer, determination of microfacies using log curve, character the connectivity of sandstones and plotting of geology map. Through the application, this system has high precision and has become a useful tool in the study of geology.