201002024 九、發明說明： 【發明所屬之技術領域】 ^ 一種通訊保密系統，特別是指一種混沌系統結合粒子 ’ 群優演算法求得比例積分控制器之重要參數，達到主從系 統之間同步的通訊保密系統及其資料傳輸方法與比例積分 控制模組設計方法。 【先前技術】 依先前的混沌通訊保密系統，其資料傳輸之加密與解 密的手段在於使傳輸裝置與接收裝置的混沌訊號同步。一 般而言，皆於接收裝置上配置比例積分控制器，以藉由比 例積分控制器來進行傳、收兩方的混沌訊號同步。 然’目前在工業上’為達成比例積分控制器 (Proportional-Integral controller ； PI controller) 高精度控制的要求，多以如下方式進行設計： 其-，依靠工程師的設計經驗、不斷的測試與各種灸 數的嘗試，力求得到最佳的比例、積分之參數。 少 法，以來計算 其二，利用模擬退火法和遺傳基因演曾 出比例積分控制器的比例、積分之參數。 然先前技術具有無法避免之缺失： 其- ’以㈣法則設計的比例積分π / 統可能會因不佳之參數設定，而使整個/态，，、受控系 及難以精準的控制，浪費的時間也&久糸统變的更不穩定 二個參數的變化又是彼此之間相二而且比例、積分 办普者’於是調整起來 5 201002024 , 相當的不容易或繁雜。 其二，模擬退火法和遺傳基因演算法皆被眾多學者分 析出自身不完善之處，可能陷入局部最佳值，也就是對於 比例、積分一個參數即存在無法取得最佳解之情形。 【發明内容】 有鑑於此’本發明所欲解決之問題係在於提供一種利 用混沌訊號本身具有的軌跡不可預測特性’並結合粒子群 # 優法快速取得比例積分控制模組的控制參數，透過此比例 積分控制模組有效將傳、收兩端的混沌訊號進行同步，利 於傳輸資料之加解密的混沌通訊保密系統及其資料傳輸方 法與比例積分控制模級設計方法。 為解決上述系統問題，本發明所提供之技術手段係揭 露一種混、;屯通訊保密系統，其包含一傳輸裝置與一接收裝 置。傳輸裝置包含一第一混洗模組、一加密模組與一傳輸 模組；接收裝置包含—接收模組、一比例積分控制模虹、 一弟二混先模組與一解密模組。 第一混沌模組用以產生第一混沌訊號’加密模組根據 第一混沌訊號加密於原始訊號形成傳輸訊號，傳輸模組係 傳送傳輸訊號與第一混沌訊號。第二混沌模組產生第二混 沌訊號，接收模組係取得傳輸訊號與第一混沌訊號，比例 積分控制模組以第一混泥訊號與第二混沌訊號為條件，利 用粒子群優演算法推算出一設定參數，並根據設定參數控 制弟二混洗模組以令第二混池訊號同步於第一混、;屯訊號， 6 201002024 再以同步的第二混沌訊號解密傳輸訊號。 為解決上述方法問題，本發明係揭露一種混沌通訊保 密系統之資料傳輸方式，係先利用一傳輸裝置之一第一混 洗模組產生一第一混丨屯訊號；根據第一混先訊號對一原始 訊號加密為一傳輸訊號，並輸出傳輸訊號與第一混沌訊 號；利用一接收裝置之一第二混沌模組產生一第二混沌訊 號；根據第一混沌訊號與第二混沌訊號，令接收裝置之一 比例積分控制模組利用一粒子群優演算法推算出一設定參 數；根據設定參數控制第二混沌模組，令第二混沌訊號與 第一混沌訊號同步；以及根據同步之第二調變電壓對傳輸 訊號解密。 為解決上述設計方法問題，本發明係揭露一種比例積 分控制模組設計方法，主要是利用粒子群優演算法取得最 佳的比例參數、與積分參數。此設計方法係先利用類隨機 序列（QRS)產生群體；透過兩混沌模組定義系統誤差以 計算性能指標；定義粒子與群體的最佳位置與適應函 數；利用慣性權重法則更新粒子並根據更新前、後的適 應函數，取最佳者；從更新的粒子，計算群體之最佳位 置與適應函數，並判斷計算前、後的適應函數，取最佳 者；重新計算性能指標，判斷群體的適應函數是否收 斂；若判斷為收斂，即根據群體的最佳位置與適應函數 計算比例增益與積分增益；以及若判斷為未收斂，即返回 利用慣性權重法則更新粒子的步驟。 7 201002024 本發明具有先前技術無法達到之功效： . 其一，混沌訊號本身具有轨跡不可預測性、白色 ,般的寬頻，以及對初始條件敏感等特性，因此產生的傳二 訊號同樣具有無法解譯之特性。 珣 其二’接收I置在取得傳送裝置混洗訊號時 構傳送裝置所有狀態並達到雙端同步，也代表著 = 產生的調變電壓會與傳送裝置一致，只需根據接置^201002024 IX. Invention: [Technical field of invention] ^ A communication security system, especially a chaotic system combined with particle 'group optimization algorithm to obtain important parameters of proportional integral controller, to achieve synchronization between master and slave systems Communication security system and its data transmission method and proportional integral control module design method. [Prior Art] According to the previous chaotic communication security system, the means for encrypting and decrypting data transmission is to synchronize the chaotic signals of the transmitting device and the receiving device. In general, a proportional-integral controller is disposed on the receiving device to synchronize the chaotic signals of the transmitting and receiving parties by the proportional integral controller. However, 'currently in the industry' to achieve the Proportional-Integral controller (PI controller) high-precision control requirements, mostly in the following ways: -, relying on the engineer's design experience, continuous testing and various moxibustion Try to get the best ratio and integral parameters. The second method has been used to calculate the second, and the parameters of the proportional integral and the integral of the proportional integral controller have been used by the simulated annealing method and the genetic algorithm. However, the prior art has an inevitable deficiency: its - 'proportional integral π / system designed by (4) rule may be set due to poor parameters, so that the whole / state,, controlled system and difficult to control accurately, wasted time Also & the long-term change of the more unstable two parameters of the change is the second and the ratio, the integral planner's then adjusted up 5 201002024, quite not easy or complicated. Second, both simulated annealing and genetic algorithm have been analyzed by many scholars for their imperfections, and may fall into local optimum values, that is, for the case of proportional and integral parameters, there is a situation in which the optimal solution cannot be obtained. SUMMARY OF THE INVENTION In view of the above, the problem to be solved by the present invention is to provide a control parameter that utilizes the unpredictable characteristics of the trajectory of the chaotic signal itself and combines the particle group # 优 method to quickly obtain the proportional integral control module. The proportional integral control module effectively synchronizes the chaotic signals at both ends of the transmission and reception, and facilitates the chaotic communication security system for encrypting and decrypting data, its data transmission method and the proportional integral control mode design method. In order to solve the above system problems, the technical means provided by the present invention discloses a hybrid communication system including a transmission device and a receiving device. The transmission device comprises a first shuffling module, an encryption module and a transmission module; the receiving device comprises a receiving module, a proportional integral control mode rainbow, a brother and a second mixing module and a decryption module. The first chaotic module is configured to generate a first chaotic signal. The encryption module encrypts the original signal according to the first chaotic signal to form a transmission signal, and the transmission module transmits the transmission signal and the first chaotic signal. The second chaotic module generates a second chaotic signal, the receiving module obtains the transmission signal and the first chaotic signal, and the proportional integral control module uses the particle swarm optimization algorithm to calculate the first hybrid signal and the second chaotic signal. A setting parameter is selected, and the second mixing module is controlled according to the setting parameter to synchronize the second mixing signal to the first mixing; the signal, 6 201002024, and then the second chaotic signal is used to decrypt the transmission signal. In order to solve the above method problem, the present invention discloses a data transmission mode of a chaotic communication security system, which first generates a first mixed signal by using a first shuffling module of a transmission device; according to the first mixed signal pair An original signal is encrypted as a transmission signal, and the transmission signal and the first chaotic signal are output; a second chaotic signal is generated by using a second chaotic module of a receiving device; and receiving is performed according to the first chaotic signal and the second chaotic signal A proportional integral control module of the device uses a particle swarm optimization algorithm to derive a set parameter; the second chaotic module is controlled according to the set parameter, so that the second chaotic signal is synchronized with the first chaotic signal; and the second tuning according to the synchronization The variable voltage decrypts the transmission signal. In order to solve the above design method problem, the present invention discloses a design method of a proportional integral control module, which mainly utilizes a particle group superior performance algorithm to obtain optimal proportional parameters and integral parameters. This design method first uses the random sequence (QRS) to generate the population; defines the system error through two chaotic modules to calculate the performance index; defines the optimal position and adaptation function of the particle and the group; uses the inertia weight rule to update the particle and according to the update And the adaptive function, the best one; from the updated particles, calculate the optimal position and adaptation function of the group, and judge the adaptive function before and after the calculation, take the best; recalculate the performance index to judge the adaptation of the group Whether the function converges; if it is judged to be convergent, the proportional gain and the integral gain are calculated according to the optimal position of the group and the adaptive function; and if it is judged that it is not converged, the step of updating the particle by the inertia weight rule is returned. 7 201002024 The present invention has the effects that the prior art cannot achieve: First, the chaotic signal itself has the characteristics of unpredictable trajectory, white, general broadband, and sensitivity to initial conditions, so the generated two signals are also unsolvable. The nature of translation.珣 The second 'receive I' is in the state of acquiring the shuffling signal of the transmitting device and all the states of the transmitting device are reached to achieve double-end synchronization. It also means that the generated modulation voltage will be consistent with the transmitting device, and only need to be connected according to ^
r生的調變電壓調整傳輸訊號，即可還原原來的原始歸 然而竊取者即使擷取到傳輪訊號， JU 統的響應狀態，恨難解回原始訊號。疋…法建構出整個系 其二’比例積分控制模組產 ^ 群優演算法，乃以類隨機序列法建二參數所使用的粒子 得比例積分控制模經的控制參數不总起始群體，故最後取 形，可更有利於混洗訊號之同步^入局部最佳解的情 提升整體系統的控制效能。一、得輪訊號之解譯，同時 i 其四，本發明主要結合粒 法，來精確地衫纽例積分法化的方 以省去煩雜之實驗步驟，避 禺、’且的麥數，不但可 素所造成的誤差，並使奸實料，可能人為因 值，使系統之響應，滿足料:能f、標⑽達到最小 其五，透過粒子群優演算法 &二农 組，可適用於各種不同之混沌/設計的比例積分控制模 構簡單、成本較低及維修:易進行控制’而且具有架 憂點’同時對於控制系統 8 201002024 保有很好的性能，又易實現。 【實施方式】 為使對本發明的目的、構造特徵及其功能有進一步的 • 了解’茲配合相關實施例及圖式詳細說明如下： 請參照圖1，其係為本發明實施例之通訊保密系統， 係包含一傳輸裝置1〇〇與一接收裝置2〇〇。 傳輸裝置100包含一第一混沌模組110、一加密模組 120、一傳輸模組130。接收裝置200包含一第二混沌模組 210、一比例積分控制模組24〇、一接收模組23〇與一解密 模組220。 第一混沌模組110與第二混沌模組210係各自產生一 第一混、;屯訊號及一第二混沌訊號。加密模組12〇係根據第 一混洗訊號對原始訊號加密形成一傳輸訊號。傳輸模組13 〇 則傳送傳輸訊號與第—混沌訊號。 接收模組230係取得第一混沌訊號與傳輸訊號，比例 、 積分控制模組240根據第二混沌訊號以及接收模組230取 得之第一混沌訊號為條件，利用一粒子群優演算法推算出 一设定參數。根據設定參數控制第二混沌模組，令第 = 號同步於第—混沌訊號。而解密模組22〇係：用 5 v勺第一混沌訊號對傳輪訊號解密，以還原成原始訊號。 凊參照圖2’其為本發明實施例之資料傳 方法包含下列步驟： ’式，，、 利用-傳輸裝置100之一第一錢模組11〇產生一第 9 201002024 一混沌訊號（步驟S210)。如前所述，傳輸裝置100在取 得原始訊號時，係利用第一混沌模組110產生第一混沌訊 號，所使用為類比混沌電路，其數學模式，以一階微分方 • 程可表示為弋i =3.6(1^2-1^)、i„,2 =^3+21^2、弋3 =1„,八2-0上„,3 與 凡=X»,2 ’其中4l、弋2、‘、為此第一混先模組1 1 0產生的三 個狀態電壓，為用以加、解密的第一混沌訊號。 根據第一混洗訊號對一原始訊號加密為一傳輸訊號， 並輸出傳輸訊號與第一混沌訊號（步驟S220 )。此步驟中， 傳輸裝置100之加密模組120可為一加法器，利用相加的 遮蔽（mask)方式來對原始訊號加密。之後，傳輸模組130 即輸出傳輸訊號與第一混沌訊號。 利用一接收裝置200之一第二混沌模組210產生一第 二混沌訊號（步驟S230 )。接收裝置200啟動時，係利用 第二混沌模組210產生第二混沌訊號，其數學模式以一階 微分方程可表不為isl二3.6(xi2-Xn)、元2=-;^八3+2尤52+吨）、 \ = XdXs2 - 0.3¾與h = xs2 ’其中凡為弟*一 '/屯5孔3虎’毛；、毛2與;為 狀態電壓。本實施例中，給予第一混沌模組110與第二混 沌模組210不同的初始值，依混沌系統之特性，故第一混 沌模組110與第二混沌模組210開始運行的訊號曲線即為 相異。 根據第一混沌訊號與第二混沌訊號，令接收裝置200 之一比例積分控制模組2 4 0利用一粒子群優演算法推算出 一設定參數（步驟S240 )。設定參數之推導即涉及比例積 201002024 分控制器的設計方式，此參數計算模型 混沌模組uo與第二混沌模組210皆 所不第一 燋。Τΐ. 白使用Lu system進行建 構Lu system的基本數學式定義如下： X ^ θγ (j/ — χ) y = -xz + 03y •έ — xy~~02z (i) 在此，㈣，θ2=3,θ3=20。接下來我們將運用一 (Operational Amplifier ； 〇PA)^t F, , ： ::我們為了配合運算放大器的規格，我們將以二 修改’如下列所示： 耳先，為加快響應速度，選定新的混洗系統如(2)所示 ^«1 =θχ{χη2 -xrol) 女w - -XmlXm3 + ^«3 =ZXm\Xm2~^2Xm •m2 (2) 其中廷取β = 3.6，θ2 = 0.3，θ3 = 2。接下來依(2试建立所提之 保密系統，如下列所示： 11 第一混沌模組110的建構式： X»1 = 3.6(xm2 - Xml) m2 rmlXm3 + ^Xm2 X' >3 = Xm\Xm2 ~ °*3Xw3 ym=xm2 (3) 第二混沌模組21〇的建構式: xsx =3.6(^2-^^,) ^2=-^Λ3+2χί2+^(〇r raw modulation voltage adjustment transmission signal, you can restore the original original return. Even if the stealer captures the transmission signal, the response status of the JU system, it is difficult to return the original signal.疋...the method constructs the whole system's two-proportional integral control module production group group optimization algorithm, which is based on the random sequence method to construct the two parameters used to obtain the proportional integral control mode control parameters are not the total starting group, Therefore, the final shape can be more conducive to the synchronization of the shuffling signal to improve the overall system control performance. First, the interpretation of the round signal, and the fourth of it, the invention mainly combines the grain method to accurately quantify the method of the integralization of the shirt to avoid the cumbersome experimental steps, avoiding, and the number of wheat, not only The error caused by the prime factor, and the actual material value, the human response value, the response of the system, the material: f, the standard (10) reaches the minimum of five, through the particle swarm optimization algorithm & Proportional integral control for various chaos/designs is simple, low cost and maintenance: easy to control 'and has a worrying point' while maintaining good performance and easy implementation for control system 8 201002024. [Embodiment] In order to make the object, structural features and functions of the present invention further, the following detailed description is made as follows: Please refer to FIG. 1 , which is a communication security system according to an embodiment of the present invention. The system includes a transmission device 1 and a receiving device 2A. The transmission device 100 includes a first chaotic module 110, an encryption module 120, and a transmission module 130. The receiving device 200 includes a second chaotic module 210, a proportional integral control module 24, a receiving module 23, and a decrypting module 220. The first chaotic module 110 and the second chaotic module 210 each generate a first mixed signal, a second signal, and a second chaotic signal. The encryption module 12 encrypts the original signal according to the first shuffling signal to form a transmission signal. The transmission module 13 传送 transmits the transmission signal and the first chaotic signal. The receiving module 230 obtains the first chaotic signal and the transmission signal, and the proportional and integral control module 240 uses a particle swarm optimization algorithm to calculate a first chaotic signal and the first chaotic signal obtained by the receiving module 230. Setting parameters. The second chaotic module is controlled according to the set parameter, so that the = sign is synchronized with the first chaotic signal. The decryption module 22 is configured to: decrypt the transmission signal with a 5 sc scoop first chaotic signal to restore the original signal. Referring to FIG. 2, a data transmission method according to an embodiment of the present invention includes the following steps: 'A type,,, and a first money module 11 of the transmission-transmission device 100 generates a 9th 201002024 chaotic signal (step S210). . As described above, the transmission device 100 generates the first chaotic signal by using the first chaotic module 110 when acquiring the original signal, and uses the analog chaotic circuit, and the mathematical mode is expressed by the first-order differential equation. i = 3.6(1^2-1^), i„, 2 =^3+21^2, 弋3 =1„, 八二-0上„,3与凡=X»,2 'where 4l,弋2. The three state voltages generated by the first mixing module 1 10 are the first chaotic signal for adding and decrypting. The original signal is encrypted as a transmission signal according to the first shuffling signal. And outputting the transmission signal and the first chaotic signal (step S220). In this step, the encryption module 120 of the transmission device 100 can be an adder, and the original signal is encrypted by using an added mask. After that, the transmission is performed. The module 130 outputs the transmission signal and the first chaotic signal. A second chaotic signal is generated by the second chaotic module 210 of the receiving device 200 (step S230). When the receiving device 200 is started, the second chaotic module is utilized. 210 generates a second chaotic signal whose mathematical mode can be expressed as a first order differential equation as isl two 3.6 (xi2-Xn) Yuan 2 = -; ^ 8 3 + 2 especially 52 + tons), \ = XdXs2 - 0.33⁄4 and h = xs2 'Where is the brother * a ' / 屯 5 holes 3 tiger 'hair;, hair 2 and; for the state In this embodiment, the initial values of the first chaotic module 110 and the second chaotic module 210 are given, and according to the characteristics of the chaotic system, the signals of the first chaotic module 110 and the second chaotic module 210 start to run. The curve is different. According to the first chaotic signal and the second chaotic signal, a proportional integral control module 240 of the receiving device 200 uses a particle group optimization algorithm to derive a setting parameter (step S240). The derivation refers to the design method of the proportional product 201002024 sub-controller. This parameter calculation model chaotic module uo and the second chaotic module 210 are not the first one. 白. White uses Lu system to construct the basic mathematical formula of Lu system The definition is as follows: X ^ θγ (j/ - χ) y = -xz + 03y • έ — xy~~02z (i) Here, (4), θ2=3, θ3=20. Next we will use one (Operational Amplifier) ; 〇PA)^t F, , : :: We will modify the specifications of the operational amplifiers The column shows: Ear first, in order to speed up the response, select a new shuffling system as shown in (2) ^«1 = θχ{χη2 -xrol) Female w - -XmlXm3 + ^«3 =ZXm\Xm2~^2Xm • m2 (2) where β is 3.6, θ2 = 0.3, and θ3 = 2. Next, (2) establish the proposed security system, as shown below: 11 Construction of the first chaotic module 110: X»1 = 3.6 (xm2 - Xml) m2 rmlXm3 + ^Xm2 X' > 3 = Xm\Xm2 ~ °*3Xw3 ym=xm2 (3) Construction of the second chaotic module 21〇: xsx =3.6(^2-^^,) ^2=-^Λ3+2χί2+^(〇
Xs3 = ^1^2 ~ 〇·3λ：. _ί3 (4) 如圖3所示，其中比例積分控制模組24〇 (ρι controller)為連續之型式，其標準型式如下 11 (5) (5)201002024 u{t) = Kp(ye(t)+J· ^ye{r)dT) 其中秦㈣-灿為系統誤差，咐為第二混雜組2i〇 之控備人（即為_毅㈣輸24Q的細奴錄），^為 比例增益’ Z為積分時間常數。（5)式亦可表示成 t 4)=Αλ(，)+Ή如 ⑹ 其中尺,.=f為比例積分控制器之積分增益。 而本發明之通訊保选糸統中’ PI controller設計問題 即在於如何選取適當的\忑二個參數，使系統具較佳之 控制性能，而典型之輸出規格中常包含最大超越量，上 升時間’穩定時間和穩態誤差等，而較常用的性能指標 有誤差平方的積分（Integral Square Error ; ISE)和誤差絕 對值的積分（integral of Absolute Error ; IAE)兩種，其數 學式定義分別如下： ISE= ζε2(τ)άτ ⑺ ΙΑΕ= ζ\ε(τ)\άτ ⑻ 其中e為系統誤差，且在本實施例中，以ISE作為性能 指標之參照。 一般而言，粒子群優演算法（PSO)是藉由模擬簡單 的個體所組成的群聚與環境及個體之間的互動行為，再 其過程中利用局部的訊息來產生不可預測的群體行 12 201002024 ^而其行為分為三大特點：朝目標移動、跟 表近之個體移動、朝向群體中心移動。在求:目“ 每-個個體稱之為粒子(Partiele)，而每個粒子的二中、， 4來表不’其中,·代錄個粒子，峨表粒子所鱗的^ 而每-個粒子移動的速度Μ來表示。另外，所有的粒有 一個相對應於最佳化問題的適應函數(〇bjec:v! function) ’所以在其過程中，每—個粒子都會知道自我 本身的最佳位置❻適應值，同時也會知道目前群體中 最佳的位置與適應值。然而在移動的過程中，有兩個極 其重要的S素：position (粒子位置）和(粒子速度）。 所以，必須透過一些更新法則來更新位置與速度，一般 最常見的更新法則有慣性權重法（Inertia weight method)、最大速度法（meth〇d)和收縮係數法 (Constriction factor method)，而本發明是利用慣性權重 法來更新’其數學式如下所示：Xs3 = ^1^2 ~ 〇·3λ:. _ί3 (4) As shown in Fig. 3, the proportional integral control module 24〇(ρι controller) is a continuous type, and its standard type is as follows 11 (5) (5) 201002024 u{t) = Kp(ye(t)+J· ^ye{r)dT) where Qin(4)-can is the systematic error, and the second mixed group 2i〇 is the control person (that is, _Yi (four) loses 24Q's fine slave record), ^ is the proportional gain 'Z is the integral time constant. Equation (5) can also be expressed as t 4)=Αλ(,)+Ή(6) where the ruler, .=f is the integral gain of the proportional integral controller. The PI controller design problem in the communication security system of the present invention lies in how to select the appropriate two parameters to make the system have better control performance, and the typical output specification often includes the maximum overshoot, and the rise time is 'stable. Time and steady-state error, etc., and the more commonly used performance indicators are the integral of the error square (Integral Square Error; ISE) and the integral of Absolute Error (IAE). The mathematical definitions are as follows: ISE = ζ ε 2 (τ) ά τ (7) ΙΑΕ = ζ \ ε (τ) \ ά τ (8) where e is a systematic error, and in this embodiment, ISE is used as a reference for performance indicators. In general, the Particle Swarm Optimization (PSO) algorithm is based on the simulation of simple individuals and the interaction between the environment and the individual, and then uses local information to generate unpredictable group rows. 201002024 ^ And its behavior is divided into three major characteristics: moving toward the target, moving with the individual near the table, moving toward the center of the group. In seeking: "Every individual is called a particle (Partiele), and each particle's two, 4, or not", in which a particle is recorded, the surface of the particle is ^ and each - The speed of particle movement is represented by Μ. In addition, all particles have an adaptation function (〇bjec:v! function) corresponding to the optimization problem. Therefore, in the process, each particle knows the most of itself. The best position is the fitness value, and it also knows the best position and fitness value in the current group. However, in the process of moving, there are two extremely important S elements: position (particle position) and (particle velocity). Location and speed must be updated through some update rules. The most common update rules are the Inertia weight method, the maximum speed method (meth〇d), and the Constriction factor method. The present invention utilizes The inertia weight method is updated to 'the mathematical expression is as follows:
Vid{h + \) = wv^ + c^-X^m + c^-X^h)) (9) xid(h+\) = xid{h)+vid{h+\) (1〇) 其中’ :第^’個粒子。 d :係數個數。 A :迭代次數。 A:每個粒子目前的位置。 4:每個粒子目前的速度。 A:目前粒子的最佳位置。 13 201002024 g:目前群體的最佳位置。 ei、4 :學習因子。 η、~ :介於〇和1之間的隨機變數。 因此’將待控制之第—混域組㈣與第二混泡模 的建構式⑺⑷，代入圖3中之控制系統方塊 Γ 粒子群優演算法之演切程，就可以容易地 传到V—參數’且依粒子群優演算法之精神，此建構 2㈣分控制器，將可以使整體系統之性能指標ISE 鈿到最小。 ，融人粒子群優演算法的觀念，可定義本發明中之 ::問題如下：即找到-組參數化伽，使得閉 2糸統之性能指標ISE最小，更精確地，此最佳化問 、以數¥式描述如下：即制—組參數^使得 (11) ISE= i:e2 ⑴dT ， 最小化。 又本發明粒子群優演算法之實施，如圖4所示，其 步驟詳細描述如下： :用類隨機序列法產生複數個粒子以形成一群 S41。)母粒子具有一粒子位置與-粒子速度（步驟 每-粒子之後需相互比較，故對應有d個變數。而且 用QRS產生的群體，較不易產生局部最佳解。 14 201002024 根據第一混沌模組110之建構式與第二混沌模組 210之建構式定義一系統誤差，根據系統誤差建構出一 性能指標公式（步驟S420)。 此步驟中，係透過Λ(〇 = Λ,(〇-凡(〇計算系統誤差，再以 ISE作為本實施例的性能指標公式，即使用公式（8)進行 計算。 計算每一粒子之一最佳位置與一適應函數，並從所 有粒子之最佳位置與適應函數選擇出一第一最佳粒子，令 第一最佳粒子之最佳位置與適應函數作為群體之第一最佳 位置與第一適應函數（步驟S430)。 先計算每個粒子的適應函數y;，而以Α表示每個粒子當時的最 佳位置與其適應函數。然計算過程中，粒子之間將不斷的比較， 以利選取當時最佳粒子來表示為(pg)群體的最佳位置及其適應函 數。 根據所有粒子之最佳位置、群體之第一最佳位置，利 用一慣性權重法則更新所有粒子之粒子位置與粒子速度 (步驟 S440)。 此步驟主要是利用方程式（9)、（10)來更新每個粒子的 位置與速度，令其成為一個新的群體。 更新每一粒子之適應函數，並根據更新之適應函數選 擇出群體之一第二最佳粒子，令第二最佳粒子之最佳位置 與適應函數作為群體之第二最佳位置與第二適應函數（步 驟 S450)。 15 201002024 定參:適二適應函數推算出二相異之設 以性數各難制第二混韻組210，再 式物二相異性能指標，並根據二相異性 岸7數（牛判斷決疋是否以第二適應函數更新第一適 應函數（步驟S460)。 此步驟中，俾蔣笛 計算出她 數與第二適應函數各別 尽，亚輸入第二混沌模組21〇以取 兩個系統誤差，κ- , ^ + 再计异出各別的性能指標，取值為小 適應函數推算的性能指標較小，即保留。若 :卢應u數推异的性能指標較小，即將其視為第一適 應函數，捨棄原第一適應函數。 3判斷第一適應函數是否收敛（步驟S470)及判斷群體 疋否達到-迭代讀（步驟s彻）。迭代次數為粒子群優 肩异法推演次數的關值。Μ斂之㈣代表在推演至一 次代數時，第-適應函數即保持—最小的怪定值。 若判斷第一適應函數收斂或判斷群體已達到一迭代次 數’根據群體之第-最佳位置與第—適應函數計算出比例 積分控制模組240之比例增益與積分增益（步驟S49〇)。 在執行以上的粒子群優演算法#，即可以得到-最佳夫 數[«广使得（7)中定義之適應函數為最小。如此便 可將參數代人（4)、（6)式中，即可使第二混域組21〇 產生的第二混沌訊號同步於第一混沌訊號。 若判斷第-適應函數未收斂，或群體未達到迭代次 16 201002024 數，即返回計算每一粒子之一最佳位置與一適應函數步 驟。直至群體達到迭代次數，或是第一適應函數收斂至一 最小值。 根據設定參數控制第二混沌模組210，令第二混沌訊 號與第一混沌訊號同步（步驟S250)。如前所述，將最佳 參數p;=[<,<]導入(4)、（6)式中，即可使第一、第二混沌 訊號形成同步。 根據同步之第二混沌訊號對傳輸訊號解密（步驟 S260)。因第二混沌訊號同步於第一混沌訊號，故解密模組 220可直接利用第二混沌訊號對傳輸訊號解密，以還原原 始訊號。 再以實驗例子驗註，首先依提出之粒子群優演算法 設定最大之迭代次數/2 = 300、族群數目d = 、權重值w = 0.8 及學習因子Cl=c2=1.5等條件，再利用 QRS產生 4 = [χ,Λ ..¾]及& = [vu,v;，2 ..·ν, 5。]，在進行演化過程之後’其 適應函數ISE之收斂曲線如圖5所示；而在演化至第20 代後，可以得到最佳適應函數值如y； =0.23922，而其對應 之最佳參數為[\, &] = [50 0.36464]如圖6所示，而第一混、;屯模 組110與第二混沌模組210也達到同步如圖7所示；再 將求得之參數，帶入利用一些運算放大器（OPA)與電阻、 電容等電子零件來實現的第一混沌模組110、第二混沌模組 210與比例積分控制模組240的電路，如圖8、9、10所示， 並於第一混洗模組110中利用遮蔽相加（mask )方式加 17 201002024 入一個訊號（妹·、^ 3戒號）傳遞，經實際測量後，其结果 •如下所述：圖Ua至圖llf所示U,在未加入比例i分 k制模組240日夺與加入後的輪出響應；圖12⑻所示為原始的注 音訊號報與其遮蔽相加（mask)後的輸出訊號，圖。 12 (b)所不為原始的語音訊號,及經由公共通道接收訊號後於 接收裝置200所解回之語音訊號。 雖然本發明以前述之較佳實施例揭露如上，然其並非 用以限定本發明’任何熟習相像技藝者，在不脫離本發明Vid{h + \) = wv^ + c^-X^m + c^-X^h)) (9) xid(h+\) = xid{h)+vid{h+\) (1〇) where ' : The first ^' particles. d : the number of coefficients. A : The number of iterations. A: The current position of each particle. 4: The current speed of each particle. A: The best position of the current particle. 13 201002024 g: The best location for the current group. Ei, 4: learning factor. η,~ : A random variable between 〇 and 1. Therefore, 'the construction of the first-mixed domain group (4) to be controlled and the second mixed-buffer mode (7)(4) can be easily transferred to V- by substituting the control system block Γ particle group optimization algorithm in Fig. 3 The parameter 'and the spirit of the particle swarm optimization algorithm, this construction 2 (four) sub-controller, will be able to minimize the overall system performance index ISE. The concept of the particle swarm optimization algorithm can be defined in the present invention: the problem is as follows: finding the group-parameter gamma, so that the performance index ISE of the closed-loop system is the smallest, more precisely, the optimization is asked. It is described as follows: the system-group parameter ^ makes (11) ISE= i:e2 (1)dT , minimized. The implementation of the particle swarm optimization algorithm of the present invention is shown in Fig. 4. The steps are described in detail as follows: A plurality of particles are generated by a random sequence-like method to form a group of S41. The mother particle has a particle position and a particle velocity (the steps need to be compared with each other after the particle, so there are corresponding d variables. Moreover, the population generated by QRS is less likely to produce a local optimal solution. 14 201002024 According to the first chaotic model The construction of the group 110 and the construction of the second chaotic module 210 define a systematic error, and a performance index formula is constructed according to the systematic error (step S420). In this step, the system passes through Λ(〇=Λ, (〇-凡(〇 Calculate the systematic error, and then use ISE as the performance index formula of this embodiment, that is, use formula (8) to calculate. Calculate the optimal position of each particle and an adaptive function, and from the optimal position of all particles The adaptive function selects a first optimal particle, and the optimal position and the adaptation function of the first optimal particle are taken as the first optimal position of the group and the first adaptive function (step S430). First, the adaptive function of each particle is calculated. y;, and Α denotes the optimal position of each particle at that time and its adaptation function. However, during the calculation process, the particles will be continuously compared to facilitate the selection of the best particles at that time to represent (pg) groups. The optimal position and its adaptation function. According to the optimal position of all particles and the first optimal position of the group, the particle position and particle velocity of all particles are updated by an inertia weighting rule (step S440). This step mainly uses the equation. (9), (10) to update the position and velocity of each particle, making it a new group. Update the adaptive function of each particle, and select one of the second best particles according to the updated adaptive function. The optimal position of the second optimal particle and the adaptation function are taken as the second best position of the group and the second adaptive function (step S450). 15 201002024 The fixed parameter: the adaptive function is used to calculate the difference between the two differences. Each of the second mixed rhyme groups 210 is difficult to determine the performance index according to the two-phase heterosexual shore number 7 (the cow judges whether or not the first adaptive function is updated with the second adaptive function (step S460). In the middle, Yan Jiangdi calculated her number and the second adaptation function separately, sub-input the second chaotic module 21〇 to take two systematic errors, κ-, ^ + then calculate the different performance indicators, take Value The performance index of the small adaptive function calculation is small, that is, it is retained. If the performance index of Lu Ying's number is different, it is regarded as the first adaptive function, and the original first adaptive function is discarded. 3 Determine whether the first adaptive function is Convergence (step S470) and judging whether the group has reached - iterative reading (step s). The number of iterations is the closing value of the number of times the particle group is optimized. The convergence (4) represents the first adaptation when deriving to an algebra. The function is kept—the smallest strange value. If it is judged that the first adaptive function converges or the judgment group has reached an iteration number, the proportional gain of the proportional integral control module 240 is calculated according to the first-optimal position of the group and the first adaptive function. And the integral gain (step S49〇). After performing the above particle swarm optimization algorithm #, it is possible to obtain the best fit number [«Guang makes the adaptation function defined in (7) to be the smallest. In this way, the second chaotic signal generated by the second mixed domain group 21〇 can be synchronized to the first chaotic signal by substituting the parameters (4) and (6). If it is judged that the first-adaptive function does not converge, or the group does not reach the number of iterations 16 201002024, it returns to calculate the optimal position of each particle and an adaptive function step. Until the group reaches the number of iterations, or the first adaptation function converges to a minimum. The second chaotic module 210 is controlled according to the set parameter to synchronize the second chaotic signal with the first chaotic signal (step S250). As described above, by introducing the optimal parameter p;=[<,<] into equations (4) and (6), the first and second chaotic signals can be synchronized. The transmission signal is decrypted according to the synchronized second chaotic signal (step S260). Since the second chaotic signal is synchronized with the first chaotic signal, the decryption module 220 can directly decrypt the transmission signal by using the second chaotic signal to restore the original signal. Then, with the experimental example test, firstly, according to the proposed particle swarm optimization algorithm, the maximum number of iterations/2 = 300, the number of ethnic groups d = , the weight value w = 0.8, and the learning factor Cl = c2 = 1.5 are set, and the QRS is reused. Produce 4 = [χ,Λ ..3⁄4] and & = [vu,v;,2 ..·ν, 5. ], after the evolution process, the convergence curve of its adaptive function ISE is shown in Figure 5; and after the 20th generation, the best adaptive function value such as y; =0.23922 can be obtained, and its corresponding optimal parameters [\, &] = [50 0.36464] as shown in FIG. 6, and the first mixed; 屯 module 110 and the second chaotic module 210 are also synchronized as shown in FIG. 7; The circuit of the first chaotic module 110, the second chaotic module 210 and the proportional integral control module 240 realized by using some operational amplifiers (OPA) and electrical components such as resistors and capacitors, as shown in FIGS. 8, 9, and 10 As shown, in the first shuffling module 110, the mask is added by masking method and adding 2010 201002024 into a signal (sister, ^ 3 ring sign), after the actual measurement, the result is as follows: U shown in Fig. Ua to Fig. 11f, the round-out response after the module is added and added without the proportional i-minute k-module 240; Figure 12 (8) shows the output of the original phonetic signal and its masked mask. Signal, map. 12 (b) is not the original voice signal, and the voice signal decoded by the receiving device 200 after receiving the signal via the public channel. Although the present invention has been disclosed in the foregoing preferred embodiments, it is not intended to limit the invention to any skilled artisan, without departing from the invention.
之精神和範圍内，所作更動與潤飾之等效替換，仍為本發 明之專利保護範圍内。 X 【圖式簡單說明】 圖1係本發明實施例之通訊保密系統； 圖2係本發明實施例之資料傳輸方式； 圖3係本發明實施例之比例積分控制模組之參數計算模 型； " 圖4係本發明實施例之粒子群優演算法之流程圖； 圖5係本發明實施例之適應函數iSE之收斂曲線示意圖； 圖6係本發明實施例之比例積分控制模組之最佳表數示音、 圖； 圖7係本發明實施例之混沌訊號同步示意圖； 圖8係本發明實施例之第一混沌模組之電路示意圖； 圖9係本發明實施例之第二混沌模組之電路示意圖； 圖10係本發明實施例之比例積分控制模組之電路示音圖. 18 201002024 圖11 a至圖11 f所示為與χ,在未加入比例積分控制模組時 與加入後的輸出響應示意圖；以及 圖12a與圖12b係本發明實施例之語音訊號的輸出響應結果 圖。 【主要元件符號說明】 100 傳輸裝置 110 第一混油i模組 120 加密模組 130 傳輸模組 200 接收裝置 210 第二混沌模組 220 解密模組 230 接收模組 240 比例積分控制模組 19Within the spirit and scope, the equivalent replacement of the modifiers and retouchings is still within the scope of patent protection of this invention. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a communication security system according to an embodiment of the present invention; FIG. 2 is a data transmission mode according to an embodiment of the present invention; FIG. 3 is a parameter calculation model of a proportional integral control module according to an embodiment of the present invention; 4 is a flow chart of a particle group optimization algorithm according to an embodiment of the present invention; FIG. 5 is a schematic diagram of a convergence curve of an adaptation function iSE according to an embodiment of the present invention; FIG. 6 is a best example of a proportional integral control module according to an embodiment of the present invention; FIG. 7 is a schematic diagram of synchronization of chaotic signals according to an embodiment of the present invention; FIG. 8 is a schematic diagram of a first chaotic module according to an embodiment of the present invention; FIG. 9 is a second chaotic module according to an embodiment of the present invention; FIG. 10 is a circuit diagram of a proportional integral control module according to an embodiment of the present invention. 18 201002024 FIG. 11 a to FIG. 11 f show the relationship between χ and 未, when the proportional integral control module is not added FIG. 12a and FIG. 12b are diagrams showing output response results of voice signals according to an embodiment of the present invention. [Main component symbol description] 100 transmission device 110 first mixed oil i module 120 encryption module 130 transmission module 200 receiving device 210 second chaotic module 220 decryption module 230 receiving module 240 proportional integral control module 19