Automatic and Parallel Optimized Learning for Neural Networks performing MIMO Applications

"An automatic and optimized approach based on. multivariate functions decomposition is presented to face. Multi-Input-Multi-Output (MIMO) applications by using. Single-Input-Single-Output (SISO) feed-forward Neural. Networks (NNs). Indeed, often the learning time and the. computational costs are too large for an effective use of MIMO. NNs. Since performing a MISO neural model by starting from. a single MIMO NN is frequently adopted in literature, the. proposed method introduces three other steps: 1) a further. decomposition; 2) a learning optimization; 3) a parallel. training to speed up the process. Starting from a MISO NN, a. collection of SISO NNs can be obtained by means a multidimensional. Single Value Decomposition (SVD). Then, a. general approach for the learning optimization of SISO NNs is. applied. It is based on the observation that the performances of. SISO NNs improve in terms of generalization and robustness. against noise under suitable learning conditions. Thus, each. SISO NN is trained and optimized by using limited training. data that allow a significant decrease of computational costs.. Moreover, a parallel architecture can be easily implemented.. Consequently, the presented approach allows to perform an. automatic conversion of MIMO NN into a collection of. parallel-optimized SISO NNs. Experimental results will be. suitably shown."