Semi-blind receiver for the fiber-wireless uplink channel

Kibangou, Alain; Favier, Gérard; Hassani, Malihe

doi:10.1109/cdc.2005.1583484

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…In his original ideas, Wiener uses (continuous-time) Laguerre OBFs (Wiener, 1958) and the model is called a Laguerre system (see also Boyd & Chua (1985)). Kibangou et al (2005b) use discrete-time Laguerre OBFs and use the name Laguerre-Volterra filters. When using generalized orthonormal basis functions (GOBFs), the names GOB-Volterra filters (Kibangou et al, 2005a) and Wiener/Volterra models (da Rosa et al, 2007) are used, amongst others.…”

Section: Wiener-schetzen Structurementioning

confidence: 99%

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

2017

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Block-oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use. Many different identification approaches were developed over the years to estimate the parameters of a wide range of block-oriented nonlinear models. One class of these approaches uses linear approximations to initialize the identification algorithm. The best linear approximation framework and the -approximation framework, or equivalent frameworks, allow the user to extract important information about the system, guide the user in selecting good candidate model structures and orders, and prove to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the different block-oriented nonlinear models that can be identified using linear approximations, and of the identification algorithms that have been developed in the past. A non-exhaustive overview of the most important other block-oriented nonlinear system identification approaches is also provided throughout this paper.

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Section: Wiener-schetzen Structurementioning

confidence: 99%

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

2017

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“…Em (Fu and Dumont, 1993) uma solução para o cálculo do pólo é obtida analiticamente maximizando a taxa de convergência do desenvolvimento ortonormal da resposta ao impulso do sistema, o que minimiza um limitante superior da norma quadrática do erro de aproximação associado a um truncamento desse desenvolvimento em um número finito de funções. Resultado análogo foi apresentado em (Tanguy et al, 1995;Tanguy et al, 2000) e posteriormente estendido para o caso de modelos de Volterra de segunda ordem em (Campello et al, 2001; e modelos de Volterra de qualquer ordem em (Campello, Favier and Amaral, 2003;Campello, Favier and Amaral, 2004;Kibangou et al, 2005b;Campello et al, 2006). Soluções para problemas similares envolvendo outras bases de funções ortonormais têm sido também investigadas (Oliveira e Silva, 1995b;Tanguy et al, 2002;Favier et al, 2003;Kibangou et al, 2003;Kibangou et al, 2005a;Kibangou et al, 2005c;da Rosa, 2005;da Rosa et al, 2006;da Rosa et al, 2007).…”

Section: Projeto Da Base De Funções Or-tonormaisunclassified

Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte A: identificação

Campello¹,

Oliveira

Amaral

2007

Sba Controle & Automação

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O presente artigo apresenta uma visão geral do estado da arte na área de identificação de sistemas utilizando modelos dinâmicos com estrutura desenvolvida através de bases de funções ortonormais, como as funções de Laguerre, Kautz ou funções ortonormais generalizadas. Discute-se as vantagens e possíveis limitações desse tipo de estrutura bem como os fundamentos matemáticos dos modelos correspondentes nos contextos de identificação linear, linear com incertezas paramétricas (identificação robusta) e não linear, incluindo uma revisão bibliográfica abrangente sobre o tema. Diferentes realizações de modelos com funções de base ortonormal, a saber, modelos lineares, de Volterra, fuzzy e neurais, são detalhadas e discutidas comparativamente em termos de capacidade de representação, parcimônia, complexidade de projeto e interpretabilidade. Aspectos práticos da identificação desses modelos são também apresentados e ilustrados através de dois casos de estudo envolvendo um processo simulado de polimerização isotérmica.
In this paper, an overview about the identification of dynamic systems using orthonormal basis function models, such as those based on Laguerre and Kautz functions, is presented. The mathematical foundations of these models as well as their advantages and limitations are discussed within the contexts of linear, robust, and nonlinear identification. The discussions comprise a broad bibliographical survey on the subject and a comparative analysis involving some specific model realizations, namely, linear, Volterra, fuzzy, and neural models within the orthonormal basis function framework. Theoretical and practical issues regarding the identification of these models are also presented and illustrated by means of two case studies related to a polymerization process

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“…There are a wide range of applications of block oriented Wiener models. They have been applied to both natural phenomena, such as the pH control process described above [18], and man made devices, such as valves [31] and power amplifiers [6,25]. Identification of Wiener systems has been an active research area for many years and there exist several identification algorithms in the literature.…”

Section: Wiener System Structurementioning

confidence: 99%

Towards Wiener system identification with minimum a priori information

Reyland¹

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The ability to construct accurate mathematical models of real systems is an important part of control systems design. A block oriented systems identification approach models the unknown system as interconnected linear and nonlinear blocks. The subject of this thesis is a particular configuration of these blocks referred to as a Wiener model. The Wiener model studied here is a cascade of an input linear block followed by a nonlinear block which then provides one output. We assume that the intermediate signal between the linear and nonlinear block is unknown, only the Wiener model input and output can be sampled. The difficulty of Wiener model identification is the interaction of the linear and nonlinear blocks. If one of the blocks is known then the intermediate signal can be produced and identification becomes much easier. Thus this thesis focuses on identification of the linear transfer function in a Wiener model. The question examined throughout the thesis is: given some "small" amount of a priori information on the nonlinear part, what can we determine about the linear part? Examples of minimal a priori information are knowledge of only one point on the nonlinear characteristic, or the sign of the output or simply that the transfer characteristic is monotonic over a certain range. Nonlinear blocks with and without memory are discussed. The contributions of this thesis are several algorithms for identifying the linear transfer function of a block oriented Wiener system. These are presented and analyzed in detail. These methods can be applied to either finite or infinite impulse response (i.e. FIR or IIR) linear blocks. Each algorithm has a carefully defined set of a priori information on the nonlinearity. Also, each approach has a set of assumptions on the input excitation.

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Semi-blind receiver for the fiber-wireless uplink channel

Cited by 5 publications

References 6 publications

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

Identificação e controle de processos via desenvolvimentos em séries ortonormais. Parte A: identificação

Towards Wiener system identification with minimum a priori information

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