標(biāo)題: Titlebook: Discrete-Time High Order Neural Control; Trained with Kalman Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Louki Book 2008 Springer-Verlag [打印本頁] 作者: 揭發(fā) 時(shí)間: 2025-3-21 18:28
書目名稱Discrete-Time High Order Neural Control影響因子(影響力)
書目名稱Discrete-Time High Order Neural Control影響因子(影響力)學(xué)科排名
書目名稱Discrete-Time High Order Neural Control網(wǎng)絡(luò)公開度
書目名稱Discrete-Time High Order Neural Control網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Discrete-Time High Order Neural Control被引頻次
書目名稱Discrete-Time High Order Neural Control被引頻次學(xué)科排名
書目名稱Discrete-Time High Order Neural Control年度引用
書目名稱Discrete-Time High Order Neural Control年度引用學(xué)科排名
書目名稱Discrete-Time High Order Neural Control讀者反饋
書目名稱Discrete-Time High Order Neural Control讀者反饋學(xué)科排名
作者: 特征 時(shí)間: 2025-3-22 00:17
The Challenges of Sustainability Ethicsberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme.作者: 大罵 時(shí)間: 2025-3-22 03:50 作者: 減去 時(shí)間: 2025-3-22 06:18
Book 2008omplex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?er作者: Capitulate 時(shí)間: 2025-3-22 09:05
The Challenges of Sustainability Ethicsneural observer trained with the EKF and the controllers are included. Finally, the applicability of the proposed design is illustrated by an example: output trajectory tracking for an induction motor.作者: 天賦 時(shí)間: 2025-3-22 14:47
Discrete-Time Output Trajectory Tracking,neural observer trained with the EKF and the controllers are included. Finally, the applicability of the proposed design is illustrated by an example: output trajectory tracking for an induction motor.作者: 天賦 時(shí)間: 2025-3-22 17:48
Discrete-Time Neural Observers,berger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme.作者: 不可磨滅 時(shí)間: 2025-3-22 22:30
Real Time Implementation,oach analyzed in Chap. 3, the Neural Bock Control Technique discussed in Chap. 4 and the modifications of the last two controllers treated in Chap. 6 to include the RHONO. All these applications was performed using a three phase induction motor.作者: 和音 時(shí)間: 2025-3-23 03:18 作者: 考古學(xué) 時(shí)間: 2025-3-23 05:44
Edgar N. Sanchez,Alma Y. Alanís,Alexander G. LoukiPresents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs.Includes supplementary material: 作者: Cryptic 時(shí)間: 2025-3-23 12:20 作者: 為寵愛 時(shí)間: 2025-3-23 17:10 作者: Abominate 時(shí)間: 2025-3-23 19:10
https://doi.org/10.1007/978-94-007-2285-9In this work, based on the neural network and feedback linearization techniques, a novel method to design robust control for a class of MIMO discretetime nonlinear uncertain systems is proposed. This method includes four different control schemes, which can be applied depending on the state vector measurement viability:作者: Mets552 時(shí)間: 2025-3-24 02:08 作者: 跳動(dòng) 時(shí)間: 2025-3-24 04:46
Conclusions and Future Work,In this work, based on the neural network and feedback linearization techniques, a novel method to design robust control for a class of MIMO discretetime nonlinear uncertain systems is proposed. This method includes four different control schemes, which can be applied depending on the state vector measurement viability:作者: 填滿 時(shí)間: 2025-3-24 07:02 作者: neutral-posture 時(shí)間: 2025-3-24 11:58
978-3-642-09695-2Springer-Verlag Berlin Heidelberg 2008作者: Kindle 時(shí)間: 2025-3-24 18:31
Sustainability Conflicts in Coastal Indiaunstructured and uncertain environment. Such a system may be named autonomous or intelligent. It would need only to be presented with a goal and would achieve its objective by learning through continuous interaction with its environment through feedback about its behavior [13]..One class of models t作者: 熔巖 時(shí)間: 2025-3-24 19:38 作者: Onerous 時(shí)間: 2025-3-25 02:32
Sustainability Conflicts in Coastal India chapter, a recurrent high order neural network is first used to identify the plant model, then based on this neural model, a discrete-time control law, which combines discrete-time block control and sliding modes techniques, is derived. The chapter also includes the respective stability analysis fo作者: CHART 時(shí)間: 2025-3-25 06:07 作者: 遺留之物 時(shí)間: 2025-3-25 10:07 作者: 否認(rèn) 時(shí)間: 2025-3-25 14:59 作者: oracle 時(shí)間: 2025-3-25 19:54 作者: synovium 時(shí)間: 2025-3-25 21:16
Sustainability Conflicts in Coastal Indiaunctions of state variables are selected recursively as virtual control inputs for lower dimension subsystems of the overall system [12]. Each backstepping stage results in a new virtual control designs from the preceding design stages. When the procedure ends, a feedback design for the true control作者: pulse-pressure 時(shí)間: 2025-3-26 01:27
Sustainability Conflicts in Coastal Indiaon of the dynamic system is named as the model. Basically there are two ways to obtain a model; it can be derived in a deductive manner using physics laws, or it can be inferred from a set of data collected during a practical experiment. The first method can be simple, but in many cases it is excess作者: cognizant 時(shí)間: 2025-3-26 08:02 作者: Grating 時(shí)間: 2025-3-26 12:02
1860-949X while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, d978-3-642-09695-2978-3-540-78289-6Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 停止償付 時(shí)間: 2025-3-26 13:02 作者: 柳樹；枯黃 時(shí)間: 2025-3-26 18:20
Discrete-Time Adaptive Neural Backstepping,unctions of state variables are selected recursively as virtual control inputs for lower dimension subsystems of the overall system [12]. Each backstepping stage results in a new virtual control designs from the preceding design stages. When the procedure ends, a feedback design for the true control作者: prodrome 時(shí)間: 2025-3-26 21:44
Discrete-Time Block Control,on of the dynamic system is named as the model. Basically there are two ways to obtain a model; it can be derived in a deductive manner using physics laws, or it can be inferred from a set of data collected during a practical experiment. The first method can be simple, but in many cases it is excess作者: 延期 時(shí)間: 2025-3-27 04:50 作者: BROOK 時(shí)間: 2025-3-27 06:34 作者: 阻撓 時(shí)間: 2025-3-27 11:04
Discrete-Time Block Control, chapter, a recurrent high order neural network is first used to identify the plant model, then based on this neural model, a discrete-time control law, which combines discrete-time block control and sliding modes techniques, is derived. The chapter also includes the respective stability analysis fo作者: Conflict 時(shí)間: 2025-3-27 16:17
Discrete-Time Neural Observers,e observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the作者: 偽善 時(shí)間: 2025-3-27 19:25
Discrete-Time Output Trajectory Tracking,RHONO. This observer is based on a discrete-time recurrent high-order neural network (RHONN), which estimates the state of the unknown plant dynamics. The learning algorithm for the RHONN is based on an EKF. Once the neural network structure is determined, the backstepping and the block control tech作者: 修正案 時(shí)間: 2025-3-28 01:21 作者: 沉思的魚 時(shí)間: 2025-3-28 04:33
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