【摘要】：組播是指一個(gè)信息源點(diǎn)傳輸?shù)蕉鄠€(gè)目標(biāo)節(jié)點(diǎn)的的信息傳輸方式,QoS(Quality of Sevice)稱為服務(wù)質(zhì)量,是一種網(wǎng)絡(luò)安全機(jī)制,用來(lái)解決網(wǎng)絡(luò)延遲和阻塞等問(wèn)題,是指網(wǎng)絡(luò)提供更高優(yōu)先服務(wù)的一種能力。隨著新型網(wǎng)絡(luò)業(yè)務(wù)大量涌現(xiàn),帶服務(wù)質(zhì)量保證的組播技術(shù)成為研究熱點(diǎn)。QoS組播路由問(wèn)題又稱Steiner樹問(wèn)題,用來(lái)使組播樹成本最小化,已被證明是NP完全問(wèn)題。選擇合適的QoS組播路由算法對(duì)于高質(zhì)量的組播通訊具有重要意義,混沌神經(jīng)網(wǎng)絡(luò)算法便是求解此類問(wèn)題的一種有效方法。以往的混沌神經(jīng)網(wǎng)絡(luò)求解QoS組播路由問(wèn)題多側(cè)重于改進(jìn)神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)提升算法性能,而忽略了對(duì)能量函數(shù)的改進(jìn),無(wú)法對(duì)輸出矩陣的“行”“列”項(xiàng)進(jìn)行嚴(yán)格約束。本文在傳統(tǒng)能量函數(shù)的基礎(chǔ)上添加了兩個(gè)新的約束項(xiàng),構(gòu)造出了新的能量函數(shù),保證了閉合路徑的有效性。將改進(jìn)能量函數(shù)與暫態(tài)混沌神經(jīng)網(wǎng)絡(luò)相結(jié)合求解QoS組播路由問(wèn)題。仿真結(jié)果表明,改進(jìn)的算法能夠有效提高網(wǎng)絡(luò)收斂到最優(yōu)解的概率和速度,且同時(shí)適用于復(fù)雜程度不同的組播網(wǎng)絡(luò)。噪聲混沌神經(jīng)網(wǎng)絡(luò)是在暫態(tài)混沌神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)上添加指數(shù)衰減的噪聲項(xiàng)得到的,具有隨機(jī)模擬退火特性。本文將改進(jìn)的能量函數(shù)與噪聲混沌神經(jīng)網(wǎng)絡(luò)相結(jié)合求解QoS組播路由問(wèn)題。仿真結(jié)果顯示,噪聲混沌神經(jīng)網(wǎng)絡(luò)可以使有效解率和最優(yōu)解率上升,但對(duì)于不同原因引起的優(yōu)化效果不佳,隨機(jī)噪聲的改善作用也有所不同。同時(shí),初始噪聲幅值與噪聲模擬退火速度必須控制在適當(dāng)?shù)姆秶鷥?nèi),否則會(huì)引起優(yōu)化效果下降。遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)既能夠表現(xiàn)出隨機(jī)混沌模擬退火又能表現(xiàn)出遲滯動(dòng)力,遲滯動(dòng)力有助于神經(jīng)網(wǎng)絡(luò)跳出局部極值,而在此基礎(chǔ)上得到的基于噪聲調(diào)節(jié)因子的遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)可實(shí)現(xiàn)對(duì)隨機(jī)噪聲水平的控制。本文將遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)、基于噪聲調(diào)節(jié)因子的遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)和改進(jìn)的能量函數(shù)應(yīng)用于QoS組播路由問(wèn)題。仿真結(jié)果表明,高噪聲條件下,逆時(shí)遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)的優(yōu)化結(jié)果優(yōu)于噪聲混沌神經(jīng)網(wǎng)絡(luò),而在低噪聲條件下,應(yīng)采用順時(shí)遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)改善優(yōu)化結(jié)果;基于噪聲調(diào)節(jié)因子的遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)擁有更強(qiáng)的遲滯動(dòng)態(tài),無(wú)論噪聲水平高低,都能通過(guò)控制噪聲調(diào)節(jié)因子獲得優(yōu)于遲滯噪聲混沌神經(jīng)網(wǎng)絡(luò)和噪聲混沌神經(jīng)網(wǎng)絡(luò)的優(yōu)化效果。
[Abstract]:Multicast refers to the information transmission mode (, QoS (Quality of Sevice) from one information source point to multiple target nodes called quality of Service (QoS). It is a network security mechanism used to solve the problems of network delay and congestion. It refers to the ability of the network to provide higher priority services. With the emergence of new network services, multicast technology with quality of service (QoS) assurance has become a research hotspot. QoS multicast routing problem, also known as Steiner tree problem, has been proved to be a complete NP problem to minimize the cost of multicast tree. It is very important to select suitable QoS multicast routing algorithm for high quality multicast communication. Chaotic neural network is an effective method to solve this kind of problem. In the past, chaotic neural networks used to solve QoS multicast routing problems focused on improving the performance of the neural network structure, but neglected the improvement of the energy function, and could not strictly constrain the "row" column of the output matrix. In this paper, two new constraints are added to the traditional energy function, and a new energy function is constructed to ensure the validity of the closed path. The improved energy function and the transient chaotic neural network are combined to solve the QoS multicast routing problem. Simulation results show that the improved algorithm can effectively improve the probability and speed of convergence to the optimal solution, and it is also suitable for multicast networks with different complexity. Noise chaotic neural network is obtained by adding exponentially attenuated noise term on the basis of transient chaotic neural network. It has the property of stochastic simulated annealing. In this paper, the improved energy function and the noisy chaotic neural network are combined to solve the QoS multicast routing problem. The simulation results show that the noise chaotic neural network can increase the efficient and optimal solution rates, but the improvement effect of random noise is different for different reasons. At the same time, the initial noise amplitude and the simulated annealing speed of noise must be controlled within a proper range, otherwise the optimization effect will be reduced. The hysteresis noise chaotic neural network can show both stochastic chaotic simulated annealing and hysteresis dynamics, which can help the neural network to jump out of the local extremum. The chaotic neural network based on noise regulation factor can control the random noise level. In this paper, the hysteretic noise chaotic neural network, the hysteretic noise chaotic neural network based on noise regulation factor and the improved energy function are applied to the QoS multicast routing problem. The simulation results show that the optimization results of chaotic neural networks with inverse hysteretic noise are better than those with noisy chaotic neural networks under high noise conditions, but under low noise conditions, the chaotic neural networks with time-delay noise should be used to improve the optimization results. The hysteretic noise chaotic neural network based on noise regulation factor has stronger hysteresis dynamics, regardless of the level of noise. By controlling the noise regulation factor, the optimization results are better than those of hysteretic noise chaotic neural network and noise chaotic neural network.
【學(xué)位授予單位】：齊齊哈爾大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP183;TP393.03

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