If you are acquainted with neural networks, automatic control problems are good industrial applications and have a dynamic or evolutionary nature lacking in static pattern-recognition; control ideas are also prevalent in the study of the natural neural networks found in animals and human beings. If you are interested in the practice and theory of control, artificial neu- ral networks offer a way to synthesize nonlinear controllers, filters, state observers and system identifiers using a parallel method of computation.
標(biāo)簽： Control Systems Neural For
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
自適應(yīng)(Adaptive)神經(jīng)網(wǎng)絡(luò)源程序 The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the approximation of a scalar nonlinear function of 4 variables
標(biāo)簽： collection implement Adaptive adaptive
上傳時(shí)間： 2015-04-09
上傳用戶(hù)：ywqaxiwang
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included
標(biāo)簽： Neural collection implement Adaptive
上傳時(shí)間： 2013-12-23
上傳用戶(hù)：teddysha
The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.
標(biāo)簽： Neural collection implement Adaptive
上傳時(shí)間： 2015-12-01
上傳用戶(hù)：181992417
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches.
標(biāo)簽： backpropagation implementating Lightweight lightweight
上傳時(shí)間： 2013-12-27
上傳用戶(hù)：清風(fēng)冷雨
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
標(biāo)簽： foundations The consists sections
上傳時(shí)間： 2017-06-22
上傳用戶(hù)：lps11188
Abstract—Stable direct and indirect decentralized adaptive radial basis neural network controllers are presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. Due to the functional approximation capabilities of radial basis neural networks, the dynamics for each subsystem are not required to be linear in a set of unknown coeffi cients as is typically required in decentralized adaptive schemes. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds.
標(biāo)簽： decentralized controllers Abstract adaptive
上傳時(shí)間： 2017-08-17
上傳用戶(hù)：gdgzhym
The idea of writing this book arose from the need to investigate the main principles of modern power electronic control strategies, using fuzzy logic and neural networks, for research and teaching. Primarily, the book aims to be a quick learning guide for postgraduate/undergraduate students or design engineers interested in learning the fundamentals of modern control of drives and power systems in conjunction with the powerful design methodology based on VHDL.
標(biāo)簽： Neural_and_Fuzzy_Logic_Control
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
java人工股市源碼，用了GA(Genetic Algorithm)和ANN(Artificial Neural Network)。內(nèi)附程序詳細(xì)說(shuō)明，強(qiáng)烈推薦！
上傳時(shí)間： 2015-02-26
上傳用戶(hù)：TRIFCT
Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42. 使用神經(jīng)網(wǎng)絡(luò)集成方法診斷糖尿病,肝炎,乳腺癌癥的案例研究.
標(biāo)簽： comprehensibl Description Rule-PANE accurate
上傳時(shí)間： 2013-11-30
上傳用戶(hù)：wcl168881111111
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