state transition language 中文意思是什麼
state transition language
解釋
狀態轉換語言- state : n 1 〈常作 S 〉國，國家；〈通例作 S 〉（美國、澳洲的）州；〈the States〉 美國。2 國務，政權，政府...
- transition : n 轉變，演變，變遷，變化；飛越；過渡期；【音樂】變調，轉調；【修辭學】語次轉變；【語法】轉換；【...
- language : n 1 語言；（某民族，某國的）國語；語調，措詞。2 （談話者或作者所使用的）言語，語風，文風，文體。3...
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Then we also formulate the model of computing with values via lattice - valued finite state automata, as well as the state transition function of the model of computing with words via lattice - valued finite state automata and the language accepted by lattice - valued finite automata
然後，給出了基於格值有限狀態自動機的數值計算的形式模型。同時，建立了輸入是詞的格值有限狀態自動機的轉移函數以及格值有限狀態自動機所接收的語言的定義。 -
Aiming at the design and implementation of the complex realtime control system, the thesis takes example for the intelligent manipulator, giving a structure of the hierarchical and distributed control system ( hdcs ) based on the realtime control system ( rcs ) module, advancing the analysis and design method based on the task decomposition for the complex control system, and raising to use the state transition diagrams to describe the work flow of every control task. besides, the thesis illustrates how to realize the state transition diagrams by the state tables. the state tables convert the state transition diagrams into computer language, like c + +, so as to complete the control task
本文針對復雜實時控制系統的設計與實現，以智能機械手控制系統為實例，給出一種基於實時控制系統rcs （ realtimecontrolsystem ，簡稱rcs ）模塊的遞階分佈控制系統結構；提出基於任務分解的復雜控制系統的分析與設計方法；提出用狀態轉換圖描述控制任務的實現流程，並由狀態表將狀態轉換圖轉化為計算機能夠處理的程序設計語言（如c + + ） ，以此實現整個控制任務。 -
This algorithm recovers the absence of the empiric in the case of the fixed - topology network and generates an optimal topology automatically. we end this chapter with some problems in the future. in chapter 2, we present an evolution strategy to infer fuzzy finite - state automaton, the fitness function of a generated automaton with respect to the set of examples of a fuzzy language, the representation of the transition and the output of the automaton and the simple mutation operators that work on these representations are given
目前，國內外對神經網路與自動機的結合的研究己取得了一系列成果；在第一章，我們首先將對這些結果以及這個領域的研究思想與方法做一個概要的介紹；然後提出一種推導模糊有限狀態自動機的構造性演算法，解決了模擬實驗中所給出的具體網路的隱藏層神經元個數的確定問題；在實驗中，我們首先將樣本輸入帶1個隱藏層神經元的反饋網路訓練， 150個紀元以後增加神經元，此時的新網路在124紀元時收斂；而blanco [ 3 ]的固定性網路學習好相同的樣本需要432個紀元。
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