本文選題：預測控制 + 癲癇��； 參考：《天津大學》2014年碩士論文

【摘要】：癲癇作為一種嚴重危害人類健康的常見神經系統(tǒng)疾病,是由大腦局部病變引起的。目前基于模型的研究是許多致力于癲癇疾病控制研究的科學家最青睞的研究方法。這些基于模型的理論研究的目的是找到一個控制律使特定的性能指標最優(yōu),目前最吸引人的是最優(yōu)控制對神經系統(tǒng)疾病的控制研究。本文的目標是對癲癇疾病的模型實施預測控制,預測控制不僅保留了最優(yōu)控制中性能指標最優(yōu)的特點,而且利用在線滾動優(yōu)化過程彌補了最優(yōu)控制中全局優(yōu)化的不足,并且預測控制為閉環(huán)控制,可以改善臨床上神經系統(tǒng)疾病的開環(huán)刺激效果。本論文的研究是基于計算模型的,這是理解疾病的最快,最簡便有效的研究階段。通過對能夠代表癲癇特性的多維電導間室模型,一維相模型和神經網絡模型的預測控制,實現了神經元和神經網絡的放電模式的控制,具體研究包括以下內容:首先,本文利用兩層控制算法——輸入輸出廣義預測控制實現癲癇神經元放電模式的控制。對神經元模型施加兩種控制策略:控制加在胞體和控制加在樹突的控制策略。兩種控制策略對Pinsky-Rinzel(PR)模型模擬的正常和癲癇狀態(tài)下的放電模式的預測控制均得到很好的效果。同時將此控制算法的控制性能指標與單獨用輸入輸出線性化控制的兩種控制策略進行比較。其次,利用預測控制實現一維簡化相模型的癲癇神經元放電相位控制。利用PR模型的相響應曲線分別得到其正常狀態(tài)下和癲癇狀態(tài)下的相模型�；谳斎胼敵鰪V義預測控制實現對癲癇狀態(tài)下相模型的相位的控制,使其跟蹤正常狀態(tài)是相模型的相位。最后,實現癲癇狀態(tài)的預測控制。研究了癲癇疾病與小世界網絡和平均場電位的關系,建立了Hindmarsh-Rose(HR)神經元小世界網絡模型。將代表癲癇狀態(tài)的小世界網絡同步放電模式的平均場電位控制為代表正常狀態(tài)的小世界網絡去同步放電模式的平均場電位。為了驗證此控制算法的有效性,又將小世界網絡不同步放電模式控制為同步放電模式,實現了神經網絡的同步場電位和去同步場電位之間的轉換。本文的研究對神經系統(tǒng)疾病治療的研究提供了思路,對神經系統(tǒng)疾病控制問題的硬件實現提供了理論依據,為神經系統(tǒng)疾病治療的體外研究,活體研究和臨床研究提供了重要的理論價值。
[Abstract]:Epilepsy, as a common nervous system disease that seriously endangers human health, is caused by local lesions of the brain.At present, model-based research is the preferred research method for many scientists devoted to epileptic disease control.The purpose of these model-based theoretical studies is to find a control law that optimizes certain performance indicators. At present, the most attractive is the optimal control of nervous system diseases.The objective of this paper is to implement predictive control on the model of epilepsy disease. Predictive control not only retains the characteristics of optimal performance index in optimal control, but also makes up for the deficiency of global optimization in optimal control by on-line rolling optimization process.The predictive control is closed-loop control, which can improve the effect of open loop stimulation in clinical nervous system diseases.The research in this paper is based on the computational model, which is the fastest, most convenient and effective stage to understand disease.Through predictive control of multi-dimensional conductance chamber model, one-dimensional phase model and neural network model, which can represent the characteristics of epilepsy, the discharge mode of neuron and neural network is controlled. The specific research includes the following: first,In this paper, a two-layer control algorithm, I / O generalized predictive control, is used to control the discharge pattern of epileptic neurons.Two control strategies were applied to the neuron model: control on the cell body and control on the dendrite.The predictive control of the normal and epileptic discharge patterns simulated by the Pinsky-Rinzelberg model is well achieved by the two control strategies.At the same time, the control performance index of this control algorithm is compared with the two control strategies using input and output linearization control alone.Secondly, predictive control is used to control the discharge phase of epileptic neurons in one-dimensional simplified phase model.The phase response curves of PR model are used to obtain the phase models under normal state and epileptic state respectively.The phase control of the phase model in epileptic state is realized based on the input and output generalized predictive control (GPC) so that the phase of the phase model can be traced to the normal state.Finally, predictive control of epileptic status is realized.The relationship between epilepsy and small-world network and mean field potential is studied. The model of Hindmarsh-RoseHR-neuron small-world network is established.The mean field potential of the synchronous discharge mode of the small world network which represents the epileptic state is controlled as the average field potential of the de-synchronous discharge mode of the small world network representing the normal state.In order to verify the effectiveness of this control algorithm, the non-synchronous discharge mode of small-world network is controlled as synchronous discharge mode, and the conversion between synchronous field potential and de-synchronous field potential of neural network is realized.The research in this paper provides a theoretical basis for the research on the treatment of nervous system diseases and the hardware realization of the disease control problems of the nervous system, and provides a theoretical basis for the in vitro study of the treatment of the diseases of the nervous system.In vivo and clinical studies provide important theoretical value.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：R742.1

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