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Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware.
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Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware.
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M.Tech in VLSI Design and Embedded Systems from R.V. College of Engineering, Bangalore.
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Design & Implementation of Feed Forward Neural Network for FIR Filter
Verlag:
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Waseem MohammedM.Tech in VLSI Design and Embedded Systems from R.V. College of Engineering, Bangalore.Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampli. Bestandsnummer des Verkäufers 5156884
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Design & Implementation of Feed Forward Neural Network for FIR Filter
Verlag:
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware. 72 pp. Englisch. Bestandsnummer des Verkäufers 9783659449239
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Design & Implementation of Feed Forward Neural Network for FIR Filter
Verlag:
LAP LAMBERT Academic Publishing Aug 2013, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware.Books on Demand GmbH, Überseering 33, 22297 Hamburg 72 pp. Englisch. Bestandsnummer des Verkäufers 9783659449239
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Design & Implementation of Feed Forward Neural Network for FIR Filter
Verlag:
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware. Bestandsnummer des Verkäufers 9783659449239
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Design & Implementation of Feed Forward Neural Network for Fir Filter
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
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Zustand: New. PRINT ON DEMAND pp. 72. Bestandsnummer des Verkäufers 18128813644
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Design & Implementation of Feed Forward Neural Network for Fir Filter
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
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Design & Implementation of Feed Forward Neural Network for Fir Filter
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2013
ISBN 10: 3659449237
ISBN 13: 9783659449239
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Zustand: New. Print on Demand pp. 72 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam. Bestandsnummer des Verkäufers 131741081
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