An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification

By Satchidananda Dehuri, Rahul Roy, Sung-Bae Cho, Ashish Ghosh. 2012

136 citations recorded by the source corpus.

Highlights? A novel HONs for classification task of data mining. ? A novel improved PSO for training FLANN (ISO-FLANN). ? A new complex medical domain dataset is introduced for validating the method. Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN=ISO-FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).

Subfields: Artificial Intelligence, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

In Control and Systems Engineering

1. Pradipta Kishore Dash
2. Ranjeeta Bisoi
3. Ajit Kumar Rout
4. Rajashree Dash

Citations in the corpus

• Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach (2015, methodology): “A wide variety of FLANNs with functional expansion using orthogonal trigonometric functions (Dehuri et al., 2012; Mili and Hamdi, 2012; Patra et al., 2009), using Chebyshev polynomial (Mishra et al., 2009; Jiang et al., 2012; Li et al., 2012), using Laguerre polynomial (Chandra et al., 2009) and…”