%0 Thesis %1 Esparcia-Alcazar:1998:thesis %A Esparcia-Alcazar, Anna I. %D 1998 %K algorithms, genetic programming %T Genetic Programming for Adaptive Signal Processing %U http://www.iti.upv.es/~anna/papers/Thesis.zip %X This thesis is devoted to presenting the application of the Genetic Programming (GP) paradigm to a class of Digital Signal Processing (DSP) problems. Its main contributions are a new methodology for representing Discrete-Time Dynamic Systems (DDS) as expression trees. The objective is the state space specification of DDSs: the behaviour of a system for a time instant t_0 is completely accounted for given the inputs to the system and also a set of quantities which specify the state of the system. This means that the proposed method must incorporate a form of memory that will handle this information. For this purpose a number of node types and associated data structures are defined. These will allow for the implementation of local and time recursion and also other specific functions, such as the sigmoid commonly encountered in neural networks. An example is given by representing a recurrent NN as an expression tree. a new approach to the channel equalisation problem. A survey of existing methods for channel equalisation reveals that the main shortcoming of these techniques is that they rely on the assumption of a particular structure or model for the system addressed. This implies that knowledge about the system is available; otherwise the solution obtained will have a poor performance because it was not well matched to the problem. This gives a main motivation for applying GP to channel equalisation, which is done in this work for the first time. Firstly, to provide a unified technique for a wide class of problems, including those which are poorly understood; and secondly, to find alternative solutions to those problems which have been successfully addressed by existing techniques. In particular, in the equalisation of nonlinear channels, which have been mainly addressed with Neural Networks and various adaptation algorithms, the proposed GP approach presents itself as an interesting alternative.

@phdthesis{Esparcia-Alcazar:1998:thesis, abstract = {This thesis is devoted to presenting the application of the Genetic Programming (GP) paradigm to a class of Digital Signal Processing (DSP) problems. Its main contributions are a new methodology for representing Discrete-Time Dynamic Systems (DDS) as expression trees. The objective is the state space specification of DDSs: the behaviour of a system for a time instant t_0 is completely accounted for given the inputs to the system and also a set of quantities which specify the state of the system. This means that the proposed method must incorporate a form of memory that will handle this information. For this purpose a number of node types and associated data structures are defined. These will allow for the implementation of local and time recursion and also other specific functions, such as the sigmoid commonly encountered in neural networks. An example is given by representing a recurrent NN as an expression tree. a new approach to the channel equalisation problem. A survey of existing methods for channel equalisation reveals that the main shortcoming of these techniques is that they rely on the assumption of a particular structure or model for the system addressed. This implies that knowledge about the system is available; otherwise the solution obtained will have a poor performance because it was not well matched to the problem. This gives a main motivation for applying GP to channel equalisation, which is done in this work for the first time. Firstly, to provide a unified technique for a wide class of problems, including those which are poorly understood; and secondly, to find alternative solutions to those problems which have been successfully addressed by existing techniques. In particular, in the equalisation of nonlinear channels, which have been mainly addressed with Neural Networks and various adaptation algorithms, the proposed GP approach presents itself as an interesting alternative.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Esparcia-Alcazar, Anna I.}, biburl = {https://www.bibsonomy.org/bibtex/26ebdc581e23e553c5882089ceaff5e18/brazovayeye}, interhash = {b95e047abdeea873212167644feb88fe}, intrahash = {6ebdc581e23e553c5882089ceaff5e18}, keywords = {algorithms, genetic programming}, month = {July}, school = {Electronics and Electrical Engineering, University of Glasgow}, size = {142 pages}, timestamp = {2008-06-19T17:39:18.000+0200}, title = {Genetic Programming for Adaptive Signal Processing}, url = {http://www.iti.upv.es/~anna/papers/Thesis.zip}, year = 1998 }