Image classification in remote sensing using functional link neural networks

Liu, L.M.; Manry, M.T.; Amar, F.; Dawson, M.S.; Fung, A.K.

doi:10.1109/iai.1994.336685

Cited by 15 publications

(2 citation statements)

References 8 publications

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“…Functional Link Neural Network (FLNN) is a class of Higher Order Neural Networks (HONNs) that utilize higher combination of its inputs [5,9]. It was created by Pao [9] and has been successfully used in many applications such as system identification [10][11][12][13][14][15], channel equalization [3], classification [16][17][18][19], pattern recognition [20,21] and prediction [22,23]. In this paper, we would discuss on the FLNN for the classification task.…”

Section: Functional Link Neural Network (Flnn)mentioning

confidence: 99%

Using Artificial Bee Colony to Improve Functional Link Neural Network Training

Hassim

Ghazali

2012

AMM

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Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.

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Section: Functional Link Neural Network (Flnn)mentioning

confidence: 99%

Using Artificial Bee Colony to Improve Functional Link Neural Network Training

Hassim

Ghazali

2012

AMM

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“…Functional Link Neural Network is a class of HONNs created by Pao [7] and has been successfully used in many applications such as system identification [9][10][11][12][13][14], channel equalization [3], classification [15][16][17][18], pattern recognition [19,20] and prediction [21,22]. In this paper, we would discuss on the FLNN for the prediction task.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

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