Abstract:
The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a novel tensor-product based approach ...Show MoreMetadata
Abstract:
The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a novel tensor-product based approach for representation of neural networks (NNs) is proposed. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the higher order singular value decomposition (HOSVD). The HOSVD as well as the tensor-product based representation of NNs will be discussed in detail.
Date of Conference: 23-25 June 2011
Date Added to IEEE Xplore: 14 July 2011
ISBN Information:
Print ISSN: 1543-9259
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Network Representation ,
- Neural Network Representation ,
- Singular Value ,
- Singular Value Decomposition ,
- Framework Of This Paper ,
- Discretion ,
- Artificial Neural Network ,
- Parameter Space ,
- Nonlinear Systems ,
- Parameter Vector ,
- Numerical Approach ,
- Vector Function ,
- Tensor Product ,
- Linear Algebra ,
- Trigonometric Functions ,
- Orthogonal Polynomials ,
- Dimensional Tensor ,
- Higher-order Tensors ,
- Linear Parameter Varying
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Network Representation ,
- Neural Network Representation ,
- Singular Value ,
- Singular Value Decomposition ,
- Framework Of This Paper ,
- Discretion ,
- Artificial Neural Network ,
- Parameter Space ,
- Nonlinear Systems ,
- Parameter Vector ,
- Numerical Approach ,
- Vector Function ,
- Tensor Product ,
- Linear Algebra ,
- Trigonometric Functions ,
- Orthogonal Polynomials ,
- Dimensional Tensor ,
- Higher-order Tensors ,
- Linear Parameter Varying
