Yana Mazwin Mohmad Hassim

Using Artificial Bee Colony to Improve Functional Link Neural Network Training

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.

Optimizing Functional Link Neural Network Learning Using Modified Bee Colony on Multi-class Classifications

Functional Link Neural Network (FLNN) has emerged as an important tool for solving classification problems and widely applied in many engineering and scientific problems. FLNN is known to be conveniently used as compared to ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. However, BP-learning algorithm has difficulties such as trapping in local minima and slow convergence especially for solving non-linearly separable classification problems. In this work, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN is expected to give a better accuracy result for the classification tasks.

A Modified Artificial Bee Colony Optimization for Functional Link Neural Network Training

Functional Link Neural Network (FLNN) has becoming as an important tool for solving non-linear classification problem. This is due to its modest architecture which required less tunable weights for learning as compared to the standard multilayer feed forward network. The most common learning scheme for tuning the weight in FLNN is a Backpropagation (BP-learning) algorithm. However, the learning method by BP-learning algorithm tends to easily get trapped in local minima which affect the performance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony (mABC) as a learning scheme for training the FLNN network in overcoming the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network.

Improving Functional Link Neural Network Learning Scheme for Mammographic Classification

Functional Link Neural Network (FLNN) has become as an important tool used in classification tasks due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network. Since FLNN uses Backpropagation algorithm as the standard learning scheme, the method however prone to get trapped in local minima which affect its classification performance. This paper proposed the implementation of modified Bee-Firefly algorithm as an alternative learning scheme for FLNN for the task of mammographic mass classification. The implementation of the proposed learning scheme demonstrated that the FLNN can successfully perform the classification task with better accuracy result on unseen data.

Honey Bees Inspired Learning Algorithm: Nature Intelligence Can Predict Natural Disaster

Artificial bee colony (ABC) algorithm which used the honey bee intelligence behaviors, is a new learning technique comparatively attractive for solving optimization problems. Artificial Neural Network (ANN) trained with the ABC algorithm normally has poor exploration and exploitation processes due to the random and similar strategies for finding best position of foods. Global artificial bee colony (Global ABC) and Guided artificial bee colony (Guided ABC) algorithms used to produce enough exploitation and exploration strategies respectively. Here, a hybrid of Global ABC and Guided ABC is proposed called Global Guided ABC (GG-ABC) algorithm, for getting balance and robust exploitation and exploration process. The experimental result shows that the GG-ABC performed better than other algorithms for prediction of earthquake hazards.

The performance of a Recurrent HONN for temperature time series prediction

This paper presents a novel application of Recurrent HONN to forecast the future index of temperature time series data. The prediction capability of Recurrent HONN, namely the Recurrent Pi-Sigma Neural Network was tested on a five-year temperature data taken from Batu Pahat, Malaysia. The performance of the network is benchmarked against the performance of Multilayer Perceptron, and the standard Pi-Sigma Neural Network. The predictions demonstrated that Recurrent Pi-Sigma Neural Network is capable in predicting the future index of temperature series in comparison to other models. It is observed that the network is able to find an appropriate input output mapping of the chaotic temperature signals with a good performance in learning speed and generalization capability.

Solving a classification task using Functional Link Neural Networks with modified Artificial Bee Colony

Classification is one of the most frequent studies in the area of Artificial Neural Network (ANNs). The ANNs are capable of generating complex mapping between the input and the output space and thus these networks can form arbitrarily complex nonlinear decision boundaries. One of the best known types of ANNs is the Multilayer Perceptron (MLP). However, MLP usually requires a large amount of available measures in order to achieve good classification accuracy. To overcome this, a Functional Link Neural Networks (FLNN) which has single layer of trainable connection weights is used. The standard method for tuning the weight in FLNN is using a Backpropagation (BP-learning) algorithm. Still, BP-learning algorithm has difficulties such as trapping in local optima and slow convergence that can affect the FLNN performance. In this work, an Artificial Bee Colony (ABC) algorithm known to have good exploration and exploitation capabilities in searching optimal weight is used to recover the BP-learning drawbacks. With modifications on the employed and onlooker bees foraging behavior, the implementation of the modified ABC as a learning scheme for FLNN has resulted in better accuracy rate for solving classification tasks.

Functional link neural network: artificial bee colony for time series temperature prediction

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.

Functional Link Neural Network with Modified Cuckoo Search Training Algorithm for Physical Time Series Forecasting

Functional link neural network (FLNN) naturally extends the family of theoretical feedforward network structure by introducing nonlinearities in inputs patterns enhancements. It has emerged as an important tool used for function approximation, pattern recognition and time series prediction. The standard learning algorithm used for the training of FLNN is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP algorithm is it tends to easily get trapped in local minima, resulting the degrading performance of FLNN. To overcome this problem, this work proposed an alternative learning scheme for FLNN by using a Modified Cuckoo Search algorithm (MCS), and the model is called FLNN-MCS. The performance of FLNN-MCS is evaluated based on the prediction error, testing on two physical time series data; relative humidity and temperature. Simulation results have shown that the prediction performed by FLNN-MCS is much superior compared to Multilayer Perceptron and FLNN trained with BP, and FLNN trained with Artificial Bee Colony algorithm. The significant performance has proven that FLNN-MCS is capable in mapping the input-output function for the next-day ahead forecasting.

Rice crop monitoring using multirotor UAV and RGB digital camera at early stage of growth

The increasing of population in the world lead the Malaysia government to intensification the food supply for the future in efficient way. Sustainable agriculture plays a main role for maintain the food production and preserve the environment from any excessive chemical by usage of technology for the better management. The Economic Transformation Program (ETP) emphasizes on the use of technology to finest aid crop production. Drone applications in crop monitoring are increasing globally and get place among end-users. The objective of this paper is to monitor rice crop by using multirotor Unmanned Aerial Vehicle (UAV) as known as drone and RGB digital camera in Kelantan, Malaysia. This paper will present the spatial analysis using RGB imagery in paddy plot at early stage to improve the management system. Results show that the uneven ground surface is a key element in achievement the higher yield production and improving the irrigation system in the paddy field. The ground management need to take action to make sure the paddy development can be growth in a healthy condition to increase the yield.