【正文】 有特別重要的意義?；煦畿壘€是整體上穩(wěn)定與局部不穩(wěn)定相結(jié)合的結(jié)果，稱之為奇異吸引子?！按_定性”是因?yàn)樗蓛?nèi)在的原因而不是外來的噪聲或干擾所產(chǎn)生，而“隨機(jī)性”是指其不規(guī)則的、不能預(yù)測(cè)的行為，只可能用統(tǒng)計(jì)的方法描述。 分析方法 研究神經(jīng)網(wǎng)絡(luò)的 非線性動(dòng)力學(xué) 性質(zhì)，主要采用 動(dòng)力學(xué) 系統(tǒng)理論、非線性規(guī)劃理論和統(tǒng)計(jì)理論，來分析神經(jīng)網(wǎng)絡(luò)的演化過程和吸引子的性質(zhì)，探索神經(jīng)網(wǎng)絡(luò)的協(xié)同行為和集體計(jì)算功能，了解神經(jīng) 信息處理 機(jī)制。此時(shí)，學(xué)習(xí)規(guī)律的變化服從連接權(quán)值的演變方程。在監(jiān)督學(xué)習(xí)中，將訓(xùn)練樣本的數(shù)據(jù)加到網(wǎng)絡(luò)輸入端，同時(shí)將相應(yīng)的期望輸出與網(wǎng)絡(luò)輸出相比較，得到誤差信號(hào)，以此控制權(quán)值連接強(qiáng)度的調(diào)整，經(jīng)多次訓(xùn)練后收斂到一個(gè)確定的權(quán)值。 Hebb 規(guī)則認(rèn)為學(xué)習(xí)過程最終發(fā)生在神經(jīng)元之間的突觸部位，突觸的聯(lián)系強(qiáng)度隨著突 觸前后神經(jīng)元的活動(dòng)而變化。 Hopfield 網(wǎng)絡(luò)、波耳茲曼機(jī)均屬于這種類型。反傳網(wǎng)絡(luò)是一種典型的前向網(wǎng)絡(luò)。目前，已有近 40 種神經(jīng)網(wǎng)絡(luò)模型，其中有反傳網(wǎng)絡(luò)、感知器、自組織映射、 Hopfield網(wǎng)絡(luò)、波耳茲曼機(jī)、適應(yīng)諧振理論等。 1986 年進(jìn)行認(rèn)知微觀結(jié)構(gòu)地研究，提出了并行分布處理的理論。在此期間，一些人工神經(jīng)網(wǎng)絡(luò)的研究者仍然致力于這一研究，提出了適應(yīng)諧振理論（ ART 網(wǎng)）、自組織映射、認(rèn)知機(jī)網(wǎng)絡(luò)，同時(shí)進(jìn)行了神經(jīng)網(wǎng)絡(luò)數(shù)學(xué)理論的研究。 1949年，心理學(xué)家提出了突觸聯(lián)系強(qiáng)度可變的設(shè)想。它是涉及神經(jīng)科學(xué)、 思維科學(xué) 、 人工智能 、計(jì)算機(jī)科學(xué)等多個(gè)領(lǐng)域的交叉學(xué)科。網(wǎng)絡(luò)中處理單元的類型分為三類：輸入單元、輸出單元和隱單元。 （ 4）非凸性 一個(gè)系統(tǒng)的演化方向，在一定條件下將取決于某個(gè)特定的狀態(tài)函數(shù)。聯(lián)想 記憶 是非局限性的典型例子。具有閾值的神經(jīng)元構(gòu)成的網(wǎng)絡(luò)具有更好的性能，可以提高容錯(cuò)性和存儲(chǔ)容量。它是在現(xiàn)代神經(jīng)科學(xué)研究成果的基礎(chǔ)上提出的，試圖通過模擬大腦神經(jīng)網(wǎng)絡(luò)處理、記憶信息 的方式進(jìn)行信息處理。 The development of new work mathematical theory, such as: neural work dynamics, nonlinear neural field, etc. Application study can be divided into the following two categories: 1, neural work software simulation and hardware realization of research. 2, the neural work in various applications in the field of research. These areas include: pattern recognition, signal processing, knowledge engineering, expert system, optimize the bination, robot control, etc. Along with the neural work theory itself and related theory, related to the development of technology, the application of neural work will further. Development trend and research hot spot Artificial neural work characteristic of nonlinear adaptive information processing power, overe traditional artificial intelligence method for intuitive, such as mode, speech recognition, unstructured information processing of the defects in the nerve of expert system, pattern recognition and intelligent control, binatorial optimization, and forecast areas to be successful application. Artificial neural work and other traditional method unifies, will promote the artificial intelligence and information processing technology development. In recent years, the artificial neural work is on the path of human cognitive simulation further development, and fuzzy system, geic algorithm, evolution mechanism bined to form a putational intelligence, artificial intelligence is an important direction in practical application, will be developed. Information geometry will used in artificial neural work of research, to the study of the theory of the artificial neural work opens a new way. The development of the study neural puters soon, existing product to enter the market. With electronics neural puters for the development of artificial neural work to provide good conditions. Neural work in many fields has got a very good application, but the need to research is a lot. Among them, are distributed storage, parallel processing, since learning, the organization and nonlinear mapping the advantages of neural work and other technology and the integration of it follows that the hybrid method and hybrid systems, has bee a hotspot. Since the other way have their respective advantages, so will the neural work with other method, and the bination of strong points, and then can get better application effect. At present this in a neural work and fuzzy logic, expert system, geic algorithm, wavelet analysis, chaos, the rough set theory, fractal theory, theory of evidence and grey system and fusion. 漢語翻譯 人工神經(jīng)網(wǎng)絡(luò) （ ArtificialNeuralNetworks，簡寫為 ANNs）也簡稱為神經(jīng)網(wǎng)絡(luò)（ NNs）或稱作連接模型（ ConnectionistModel），它是一種模范動(dòng)物神經(jīng)網(wǎng)絡(luò)行為特征，進(jìn)行分布式并行信息處理的算法數(shù)學(xué)模型。 Hidden unit is in input and output unit, not between by external observation unit. The system The connections between neurons right value reflect the connection between the unit strength, information processing and embodied in the work said the processing unit in the connections. Artificial neural work is a kind of the procedures, and adaptability, brain style of information processing, its essence is through the work of transformation and dynamic behaviors have a kind of parallel distributed information processing function, and in different levels and imitate people cranial nerve system level of information processing function. It is involved in neuroscience, thinking science, artificial intelligence, puter science, etc DuoGe field cross discipline. Artificial neural work is used t