| 词汇 | example_english_fuzzy-logic |
| 释义 | Examples of fuzzy logicThese examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. We needed to design and train a fuzzylogic based affect recognizer that could recognize or "understand" the physiological responses of a person. This algorithm is implemented using conventional fuzzylogic. Methods that manage uncertainty and/or impreciseness, such as fuzzylogic could be used to represent the uncertainty/impreciseness and make a crisp decision. A classified review on the combination fuzzylogic-genetic algorithms bibliography. Hence, only one term in an output linguistic variable's term set can become a consequence of a fuzzylogic rule. His research interests include database mining, fuzzylogic, robust design, and use of decision trees, regression trees, and genetic algorithms in off-line quality-control applications. Automated analysis of mass spectral data using fuzzylogic classification. This fuzzylogic control algorithm was evaluated in both simulation and experiments and was compared with the conventional impedance control. Then the rule-adaptive fuzzylogic control algorithm is applied to the foot force controller. The foot force sensor provides the force feedback for the real-time fuzzylogic force control. Then we adopted an on-line rule-adaptive fuzzylogic control algorithm for walking machine leg control. In fuzzylogic there is no clear definition as to what is exactly true or false. To overcome this difficulty, the paper proposes a force reflection gain-selecting algorithm based on neural network and fuzzylogic features. For a fuzzylogic system, the rule-base defines the required outputs for any given combinations of inputs. It was decided to embed a fuzzylogic mechanism to autonomously acquire any initial knowledge from the contact states. Chapter 13 explores the representation of commonsense in the mathematical formalism of fuzzylogic. This chapter forms a sound and comprehensive introduction to fuzzy reasoning and the construction of a fuzzylogic expert system. The connective structure can be trained to develop fuzzylogic rules and to find optimal input 0output membership functions. Qualitative evaluation of engineering designs using fuzzylogic. In this paper we propose a new method for online stress detection of humans using wavelet packets decomposition and fuzzylogic. More general techniques may be required, such as production rules, probability or fuzzylogic. How can symbolic reasoning methods be used in conjunction with other approaches, such as probabilistic reasoning, fuzzylogic and numerical simulation? This vector-format fuzzylogic approach can significantly reduce the complexity in the development of fuzzy reasoning. To quantify the fuzzy output response a fuzzylogic membership value is used. A method combining a genetic algorithrn with fuzzylogic to specify the joint parameters is proposed here. The implication operation in fuzzylogic is analogous to the "intersection" operation() in the conventional logic theory. In addition, there have been much research based on probability, vision, behavior-based method and fuzzylogic. We have designed and implemented the hybrid position and force control on foot via the fuzzylogic control model, and obtained satisfactory results. Controllers using fuzzylogic have been incorporated in cameras and in such things as washing machines and other domestic electrical appliances. Results showed that the fuzzylogic controller is good and effective in controlling the leg machine. Design of sophisticated fuzzylogic controllers using genetic algorithms. His current research interests are in the areas of fuzzylogic in production problems, and total quality management. Design of an adaptive fuzzylogic controller using a genetic algorithm. The parallel nature of the rules is one of the most important aspects of fuzzylogic systems. To overcome this difficulty, the paper proposes a force reflection gain selecting algorithm based on neural network and fuzzylogic features. This fuzzylogic control algorithm is evaluated in both simulation and experiments. A fuzzylogic control algorithm is then developed and implemented to control this prototype leg. In what follows, we present in detail the development of the new fuzzylogic motion planner. Position and force are both controlled along the same direction and fuzzylogic was employed to switch from position to force control and vice versa. In order to address these problems, a fuzzylogic approach was adopted, described in the following sections. It did, however, involve a manual design process requiring significant knowledge of control theory and fuzzylogic. Using these experiments initially, we obtained pilot data to design and train a 6-input 1-output fuzzylogic system. A control algorithm is designed using fuzzylogic to control the tracking velocity which affects the magnitude of input torques. To solve this problem, a new robust controller, which does not saturate, was devised by using an algorithm based on fuzzylogic. The main idea of this algorithm is to control the tracking time by using fuzzylogic. To overcome this problem, a new control algorithm is devised: new trajectory planning based on fuzzylogic. The fuzzylogic for the robust controller introduced in section 4 is applied. System and method for providing raw mix proportioning control in a cement plant with a fuzzylogic supervisory control. In our works9, 10 the basic adaptive fuzzylogic controller has been introduced and designed. There is no absolute answer when it comes to developing fuzzylogic controllers. The described navigator is built around fuzzylogic controllers. If fuzzylogic can keep our heating and air conditioning systems in line, it should be able to control any aspect or parameter in music. Industry now contains many people who studied expert systems at university and a growing number who also studied neural nets and fuzzylogic. Inductive learning and fuzzylogic have the advantage of generating rules that are intelligible to humans, which is not the case for neural networks. The fuzzylogic supervisory controller governs this recombination by per forming soft switching between different modes of operation. Therefore, object-oriented programming languages are desirable for declarative knowledge representation, objectoriented concepts, and fuzzylogic. The second illustrates another aspect of the fuzzylogic methodology allowing for a querying by examples process. Taking inspiration from fuzzylogic in semantics, he introduces the notion of "fuzzy temporality," or temporality that is indeterminate. The result of the function is the consequent linearly weighted by the antecedent, which will usually be the result of evaluating fuzzylogic expression. The fuzzylogic itself can also be made more sophisticated to improve the switching performance. Some researchers have designed the controller based on neural fuzzylogic and genetic algorithms. In this paper, a novel vector-format fuzzylogic approach is proposed for this purpose. Among them the applications of neural networks and fuzzylogic in robot control have shown promising results and have been widely investigated. Also, fuzzylogic lets us mimic human driving behavior to some extent. A description of this type can be properly processed by using fuzzylogic. In the present application we used fuzzylogic to identify the patterns of physiological activity, as reflected in the parameters described above, indicating anxiety. As already described, attempts have been made to combine fuzzylogic and explicit robot force control. Washing machines, car braking systems, refrigeration units, and so forth all have improved controls if programmed with fuzzylogic. Results show that the fuzzylogic controller is effective in controlling the leg machine. The advantage of fuzzylogic controllers is that no comprehensive or precise information on the environment is required. An experiment in linguistic synthesis with a fuzzylogic controller. Indeed, fuzzylogic, coupled with rule-based systems, has the ability to model the approximate and imprecise reasoning processes that are common in human thinking or human problem solving. The simulations of the rule-adaptive fuzzylogic foot force control. In the examples mentioned earlier, multidimensional vectors are decomposed into a set of scalar elements, which allows the usage of traditional fuzzylogic technique to deal with spatial reasoning. The modelling library consists of neural network, fuzzylogic, genetic algorithm, mathematical analysis, and other available software. Fuzzy decision-making can be considered as a good solution to this, because fuzzylogic enables us to implement our natural language based understanding in control systems. The selector consists of four modules just as the general fuzzylogic controller does: a fuzzy decoder, a rule base, a fuzzy reasoning and a defuzzifier. Applying genetics to fuzzylogic. The step responses of the fuzzylogic force control and impedance force control. The method estimates characteristic of the master arm and the environments by using neural networks, and then, determines the force reflection gain from the estimated characteristics by using fuzzylogic. Section 2 introduces the logic part of fuzzylogic (the term 'fuzzylogic' is used to describe both the actual logic and the whole concept of fuzzy theory). It stresses the shortcomings of most theories in explaining the uncertain and fuzzylogic regulating the relationships between the intellectual side of expertise and the administrative one. The lack of standardized structure of the documents to be retrieved calls for the use of -exible tools the design of which fuzzylogic is well suited for. It can be noted that, in addition to the neural-network-based navigation, the software also allows other navigation control techniques (for example, fuzzylogic and neuro-fuzzy-based techniques) to be simulated. The method estimates characteristics of the master arm and the environments using neural networks, and then, determines the force reflection gain using fuzzylogic based upon the estimated characteristics. We use fuzzylogic because it is a well-tested method for dealing with this kind of system, provides good results, and can incorporate human procedural knowledge into control algorithms. There have been studies based on behavior-based method or fuzzylogic which tries to reduce simple, repetitive mechanical motions to make robot motions close to human motions. Soft computing and fuzzylogic. However, this weakness is intrinsic to the realities of the situation, not of fuzzylogic itself. From Wikipedia This example is from Wikipedia and may be reused under a CC BY-SA license. The generalized necessities are related with a very simple and interesting fuzzylogic we call "necessity logic". From Wikipedia This example is from Wikipedia and may be reused under a CC BY-SA license. These examples are from corpora and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors. |
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