Lu, Y., Sundararajan, N., and Saratchandran, P., Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks 9 (1998), 308--318.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in Fig. 1 corresponding to block H.Thus, it is reasonable to try to find an optimal cancellation system using nonlinear adaptive processing models and associated training methods. Methods based on nonlinear processing of the interference signal could be called nonlinear noise cancellers. In [3 5] Cha et al. and Lu et al. have demonstrated that accurate channel equalization and cancellation of interference and noise can be achieved with the use of radial basis function (RBF) neural networks and generalized radial basis function networks. However, we are going to show that it is possible to ....

Lu, Y., Sundararajan, N., and Saratchandran, P., Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans. Neural Networks 9 (1998), 308--318.

....the parameter learning phase. Most of the times, both phases are performed off line. One of the objectives of this paper is to provide a mechanism in which both structure and parameter learning are performed on line. A constructive adaptation law, based on a minimal resource allocating algorithm [5] is applied in order to online adjust the structure and parameters of the neuro fuzzy network. Concerning control applications, in most neuro fuzzy based control schemes adaptation laws are derived using a fixed structure network. The result is that these fixed structures often need a large ....

....with no hidden units and allocates a new unit whenever an unusual pattern is presented to the network [10] A drawback is that once a hidden unit is created it can never be removed. Therefore, a natural improvement is to detect and remove the hidden units contributing little to the RBFN response [5]. The method presented here is based on the Delaunay partition of the input space combined with the minimal resource allocating network (M RAN) which uses a pruning strategy to remove units together with an additional growth criterion compared to RAN. To ensure a smooth transition in the number ....

L. Yingwei and N. Sundararajan, "Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm," IEEE Transactions on Neural Networks, vol. 9, no. 2, pp. 308-318, 1998.

....unit must be added. A drawback is that once a hidden unit is created it can never be removed. Therefore, a natural improvement is to detect and remove the hidden units contributing little to the RBFN response, Fritzke (1994) Our method is based on the Minimal Resource Allocating Network, M RAN, Yingwei and Sundararajan (1998), that uses a pruning strategy to remove units together with an additional growth criterion compared to RAN. To ensure the transition in the number of hidden units due to growing and pruning is smooth, M RAN augments the basic novelty of RAN with an additional condition based on the RMS value of ....

Yingwei, Lu; Sundararajan, N., 1998, "Performance Evaluation of a Sequential Minimal Radial Basis Function (RBF) Neural Network Learning Algorithm", IEEE Transactions on Neural Networks, vol. 9, no. 2, pp. 308-318.

....optimal cancellation system can be found using nonlinear adaptive processing methods. The solution of interference cancellation problem is well known for the linear case. However, the problem is much more complicated and have not general solution for nonlinear statement. In the papers [3, 4] and [5] have been proposed to use the Radial Basis Function (RBF) neural network and Generalized Radial Basis Function (GRBF) network for nonlinear interference and noise cancellation and channel equalization. However, we are going to show that it is possible to achieve better results in nonlinear ....

Lu, Y., Sundararajan, N., Saratchandran, P. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm, IEEE Trans. Neural Networks 9 (1998), 308-318.

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Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308--318.

No context found.

Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308--318.

No context found.

Y. Lu, N. Sundararajan and P. Saratchandran, "Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm ", IEEE Trans. Neural Networks, vol.9, No.2, pp.308-318, 1998.

No context found.

Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308--318.

No context found.

Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308--318.

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