本文關(guān)鍵詞：基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)的研究　出處：《浙江大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 腦機(jī)融合 遷移學(xué)習(xí) 腦機(jī)接口 遷移強(qiáng)化學(xué)習(xí) 遷移極限學(xué)習(xí)機(jī) 動(dòng)物機(jī)器人 大鼠機(jī)器人 神經(jīng)康復(fù) 意識(shí)診斷

【摘要】：磨機(jī)融合(Brain Machine Integration ),是指通過腦機(jī)接口技術(shù),融合生物智能和機(jī)器智能的混合智能系統(tǒng),被認(rèn)為是二十一世紀(jì)最重要的前沿科技領(lǐng)域之一。近年來,隨著腦科學(xué)和人工智能的發(fā)展,腦機(jī)融合可以將生物智能(腦)與機(jī)器智能(機(jī)),通過腦機(jī)接口技術(shù)進(jìn)行有機(jī)地融合和深度地協(xié)作,進(jìn)而形成比單一生物智能或者單一機(jī)器智能,更加強(qiáng)大的混合智能新形態(tài)。同時(shí),隨著腦機(jī)融合的發(fā)展和進(jìn)步,又可以促進(jìn)腦科學(xué)、人工智能、認(rèn)知科學(xué)與臨床醫(yī)學(xué)等領(lǐng)域的理論創(chuàng)新和應(yīng)用突破,在神經(jīng)康復(fù)與動(dòng)物機(jī)器人領(lǐng)域有著重要的研究意義。作為腦機(jī)融合的重要組成部分,機(jī)器智能具有強(qiáng)大的存儲(chǔ)和運(yùn)算能力;與機(jī)器智能相比,生物智能的優(yōu)勢(shì)在于其高效低功耗的感認(rèn)知和邏輯推理能力。如何將二者的優(yōu)勢(shì)有機(jī)地融合在一些,建立更強(qiáng)大的新型智能形態(tài),是腦機(jī)融合面臨的關(guān)鍵問題和挑戰(zhàn)。針對(duì)這一關(guān)鍵問題和挑戰(zhàn),本文進(jìn)行了基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)的研究:遷移學(xué)習(xí),可以將從不同但相關(guān)的領(lǐng)域或者不同但相關(guān)的任務(wù)中學(xué)習(xí)到的知識(shí)進(jìn)行遷移和融合;腦機(jī)接口,可以在生物大腦與外圍設(shè)備之間建立直接的連接通路;因此,基于遷移學(xué)習(xí)和腦機(jī)接口的腦機(jī)融合系統(tǒng),可以將不同生物、不同領(lǐng)域、不同任務(wù)之間的信息進(jìn)行交流、知識(shí)進(jìn)行遷移、智能進(jìn)行融合。概括來說,本文從以下三個(gè)方面逐層深入地進(jìn)行了探討。首先,本文提出基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)的概念和體系結(jié)構(gòu),總體思路為:首先,模仿生物的學(xué)習(xí)過程,使機(jī)器具有能夠在不同但相似的領(lǐng)域中,解決不同但相關(guān)的問題的能力,這對(duì)于神經(jīng)調(diào)控、殘障康復(fù)等缺乏足夠多高質(zhì)量訓(xùn)練數(shù)據(jù)的領(lǐng)域具有重要的意義;其次,通過腦機(jī)融合系統(tǒng),將學(xué)到的知識(shí)在生物與生物、生物與機(jī)器、機(jī)器與機(jī)器之間進(jìn)行遷移和融合,增強(qiáng)系統(tǒng)智能決策的能力,實(shí)現(xiàn)大腦-機(jī)器-機(jī)器-大腦之間深度協(xié)作的智能增強(qiáng)系統(tǒng);此外,通過腦機(jī)融合中計(jì)算理論與方法的創(chuàng)新,可以為生物大腦運(yùn)行機(jī)制的探索提供新的思路和方法,促進(jìn)腦科學(xué)、認(rèn)知科學(xué)和臨床醫(yī)學(xué)的進(jìn)步。然后,基于本文提出的腦機(jī)融合系統(tǒng)的體系結(jié)構(gòu),針對(duì)動(dòng)物機(jī)器人這一重要研究對(duì)象,借助浙江大學(xué)的大鼠機(jī)器人平臺(tái),本文設(shè)計(jì)了基于遷移強(qiáng)化學(xué)習(xí)的大鼠機(jī)器人腦機(jī)融合系統(tǒng)。首先,將大鼠機(jī)器人迷宮導(dǎo)航問題,抽象為經(jīng)典的強(qiáng)化學(xué)習(xí)模型;然后,根據(jù)源智能體和目標(biāo)智能體是否相同、源迷宮和目標(biāo)迷宮是否相同、源任務(wù)和目標(biāo)任務(wù)是否相同,設(shè)計(jì)了基于層次化的遷移強(qiáng)化學(xué)習(xí)算法、基于策略復(fù)用的遷移強(qiáng)化學(xué)習(xí)算法、基于值函數(shù)復(fù)用的遷移強(qiáng)化學(xué)習(xí)算法和基于規(guī)則復(fù)用的遷移強(qiáng)化學(xué)習(xí)算法;接著,基于遷移強(qiáng)化學(xué)習(xí)算法,從遷移什么、如何遷移、何時(shí)遷移三個(gè)方面,詳細(xì)地描述了大鼠機(jī)器人腦機(jī)融合系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn);并從行為實(shí)驗(yàn)的角度,證明了基于遷移強(qiáng)化學(xué)習(xí)的大鼠機(jī)器人系統(tǒng)的智能增強(qiáng)性;最后,本文從計(jì)算神經(jīng)建模的角度,解釋了此腦機(jī)融合系統(tǒng)智能增強(qiáng)的神經(jīng)機(jī)理。最后,本文進(jìn)一步將基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)的研究,從以動(dòng)物為對(duì)象的實(shí)驗(yàn)室研究,拓展到以人類為對(duì)象的臨床醫(yī)學(xué)的研究。借助哈佛大學(xué)的臨床診斷和康復(fù)平臺(tái),本文設(shè)計(jì)了基于遷移極限學(xué)習(xí)機(jī)的意識(shí)診斷和調(diào)控腦機(jī)融合系統(tǒng)。首先,將大腦意識(shí)診斷和調(diào)控的問題,抽象為基于皮層腦電的清醒預(yù)測(cè)和藥物控制模型;然后,針對(duì)臨床醫(yī)學(xué)中高質(zhì)量數(shù)據(jù)不足的問題,本文設(shè)計(jì)了基于特征和參數(shù)的遷移極限學(xué)習(xí)機(jī)算法;接著,基于遷移極限學(xué)習(xí)機(jī)算法,本文設(shè)計(jì)了意識(shí)診斷和調(diào)控的腦機(jī)融合系統(tǒng);并從臨床實(shí)驗(yàn)的角度,評(píng)估了基于遷移極限學(xué)習(xí)機(jī)的人腦意識(shí)診斷和調(diào)控系統(tǒng)的有效性;最后,基于此遷移腦機(jī)融合系統(tǒng),本文發(fā)現(xiàn)了人腦意識(shí)清醒與α震蕩具有相關(guān)性,并對(duì)此神經(jīng)機(jī)理進(jìn)行了探討。綜上所述,從基于遷移強(qiáng)化學(xué)習(xí)的大鼠機(jī)器人腦機(jī)融合系統(tǒng)的研究,到基于遷移極限學(xué)習(xí)機(jī)的人腦意識(shí)診斷和調(diào)控腦機(jī)融合系統(tǒng)的研究,本文逐漸深入地論證了基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)的可行性和有效性;并且,從計(jì)算神經(jīng)建模的角度,解釋了基于遷移學(xué)習(xí)的腦機(jī)融合系統(tǒng)智能增強(qiáng)的神經(jīng)機(jī)理;此外,基于設(shè)計(jì)的腦機(jī)融合系統(tǒng)和實(shí)驗(yàn)結(jié)果,探討了大腦意識(shí)改變的神經(jīng)機(jī)理。本研究為腦機(jī)融合系統(tǒng),在動(dòng)物機(jī)器人和神經(jīng)康復(fù)領(lǐng)域中的發(fā)展和應(yīng)用,提供一種新的思路和方法。
[Abstract]:Mills (Brain Machine Integration), fusion refers to the brain machine interface technology, hybrid intelligent system integration of biological intelligence and machine intelligence, is considered to be one of the most important twenty-first Century in cutting-edge technology. In recent years, with the development of the brain science and artificial intelligence, brain machine fusion can be intelligent (brain) and machine intelligence (machine), organic integration and depth of cooperation through brain computer interface technology, and the formation of biological intelligence than single or single machine intelligence, a new form of hybrid intelligence more powerful. At the same time, along with the development and progress of brain computer integration, but also can promote brain science, artificial intelligence, cognitive science and clinical medicine the application of the theory innovation and breakthrough, has important significance in neural rehabilitation and animal robot field. As an important part of the brain machine integration, intelligent machine has powerful storage And operation ability; compared with machine intelligence, biological intelligence advantage lies in its high efficiency and low power consumption perception and logical reasoning ability. How will the organic integration of the two advantages in some, the establishment of new intelligent form more powerful, is the key problem faced by brain fusion and challenges. Aiming at the key problems and this paper studied the challenge of brain machine transfer learning based on fusion: transfer learning, can be from different but related fields or different but related tasks to learn the knowledge of migration and fusion; brain computer interface, you can establish a connection between the direct pathway of biological brain and peripheral equipment; therefore, system brain machine transfer learning and fusion based on brain computer interface, can be different in different areas, creatures, communicate information between different tasks, knowledge transfer, integration of intelligence. In general, this article from the The following three aspects are discussed deeply. Firstly, this paper puts forward the concept and system structure of brain machine transfer learning based on fusion, the general idea is: first of all, the learning process of imitating biology, so that the machine has a similar but in different areas, different but related problem solving ability, the for neural regulation, plays an important role in disability and lack of enough high quality training data; secondly, through brain computer fusion system, the learned knowledge in biology and biological, biological and machine, migration and integration between machine and machine, enhance the ability of intelligent decision system, intelligent - brain the depth of cooperation enhancement system brain - machine - machine; in addition, the innovation of theory and methods by computing the brain machine fusion, explore the operation mechanism of the biological brain can provide new ideas and methods to promote. Brain science, cognitive science and clinical medicine progress. Then, the system architecture of brain machine is presented in this paper based on the fusion of the animal robot, which is an important research object, with the help of the rat robot platform of Zhejiang University, this paper designs enhance the migration of rat brain machine robot learning system based on fusion. Firstly, the rat robot the maze navigation problem, the abstract for reinforcement learning the classical model; then, according to the source and the target of intelligent agent is the same, the source and target is the same as the maze maze, the source and target tasks are the same, the design of the migration of hierarchical reinforcement learning algorithm based on migration strategy multiplexing reinforcement learning algorithm based on the transfer of value function multiplexing reinforcement learning algorithm and migration rule reuse based on a reinforcement learning algorithm based on migration; then, a reinforcement learning algorithm based on migration from what, how to transfer, When the transfer of three aspects, a detailed description of the design and implementation of the system of rat brain machine robot fusion; and from the behavior experiment angle, demonstrate the enhancement of intelligent robot system enhance the migration of rat based on learning; finally, this paper calculated from neural modeling perspective, explained the neural mechanism of brain machine fusion system intelligent enhancement. Finally, this paper will further study the system of brain machine transfer learning based on fusion, from the animal laboratory of the research object, to expand the clinical medicine research object to humans. By means of clinical diagnosis and rehabilitation platform of Harvard University, this paper designed the diagnosis and control of consciousness brain machine fusion system migration limit based on machine learning. First of all, the problem of consciousness diagnosis and regulation of the brain, as a model to predict and control the abstract awake cortical EEG based on drugs; then, according to the clinical medicine in high quality The problem of lack of data, this paper designed a machine learning algorithm based on the features and parameters of the migration limit; then, the migration algorithm based on extreme learning machine, this paper designs the system of diagnosis and control of consciousness brain machine fusion; and from clinical experimental perspective, the evaluation of the effectiveness of human consciousness diagnosis and control system of the migration of extreme learning machine finally, based on this migration; brain machine fusion system based on this paper, found a correlation between human consciousness and a concussion, and the neural mechanism was discussed. To sum up, from the research system of rat brain machine learning robot fusion based on System Research to strengthen the migration, human consciousness diagnosis and regulation of brain machine migration extreme learning machine the fusion based on this paper has gradually demonstrated the feasibility of the system of brain machine transfer learning and effective fusion based on neural computation and modeling; from the angle of solution The release mechanism of nerve system intelligent enhanced brain machine transfer learning based fusion; in addition, the system and the experimental results of brain machine design based on the combination of neural mechanism of brain consciousness change. This study fusion system for brain machine development and application in animal robot and rehabilitation in the field, to provide ideas and methods new.

【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：R318;TP242

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