Function Approximation With Multilayered Perceptrons Using L1 Criterion

Kaedah ralat kuasa dua terkecil atau kaedah kriteria L2 biasanya digunakan bagi persoalan penghampiran fungsian dan pengitlakan di dalam algoritma perambatan balik ralat. Tujuan kajian ini adalah untuk mempersembahkan suatu kriteria ralat mutlak terkecil bagi perambatan balik sigmoid selain daripada kriteria ralat kuasa dua terkecil yang biasa digunakan. Kami membentangkan struktur fungsi ralat untuk diminimumkan serta hasil pembezaan terhadap pemberat yang akan dikemaskinikan. Tumpuan ·kajian ini ialah terhadap model perseptron multilapisan yang mempunyai satu lapisan tersembunyi tetapi perlaksanaannya boleh dilanjutkan kepada model yang mempunyai dua atau lebih lapisan tersembunyi. The least squares error or L2 criterion approach has been commonly used in functional approximation and generalization in the error backpropagation algorithm. The purpose of this study is to present an absolute error criterion for the sigmoidal backpropagatioll I rather than the usual least squares error criterion. We present the structure of the error function to be minimized and its derivatives with respect to the weights to be updated. The focus in the study is on the single hidden layer multilayer perceptron (MLP) but the implementation may be extended to include two or more hidden layers

A study on an extended Prey-Predator algorithm

Metaheuristic algorithms are approximate solution methods for optimisation problems which try to improve the quality of solution at hand iteratively in a random way. In recent years, various studies have been conducted in forming new metaheuristic algorithms and modifying or improving existing algorithms to enhance the performance in optimal solution search. In this study, we focus on extending an existing algorithm Prey-Predator algorithm proposed by Tilahun and Ong. Prey-Predator algorithm is a metaheuristic algorithm inspired by interaction between prey and predator among animals. The algorithm imitates the way a predator runs after and hunts its preys where each prey tries to stay with the pack trying to search for hiding place and run away from the predator. In extension of Prey-Predator algorithm, the number of both best preys and predators are increased resulting in a more reasonably exploitation and exploration so that multiple solutions can be achieved. The simulation of nmPPA is carried on ten selected benchmarks test function. nmPPA aimed to solve the problem of objective values being trapped in local optimum and to find multiple solutions at the same time

Modified Firefly Algorithm

Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time

Fuzzy Preference Incorporated Evolutionary Algorithm for Multiobjective Optimization

Multiobjective evolutionary method is a way to overcome the limitation of the classical methods, by finding multiple solutions within a single run of the solution procedure. The aim of having a solution method for multiobjective optimization problem is to help the decision maker in getting the best solution. Usually the decision maker is not interested in a diverse set of Pareto optimal points. So, it is necessary to incorporate the decision maker’s preference so that the algorithm gives out alternative solutions around the decision maker’s preference. The problem in incorporating the decision maker’s preference is that the decision maker may not have a solid guide line in comparing tradeoffs of objectives. However, it is easy for the decision maker to compare in a fuzzy way. This paper discusses on incorporating a fuzzy tradeoffs in the evolutionary algorithm to zoom out the region where the decision maker’s preference lies. By using test functions it has shown that it is possible to give points in the region on the Pareto front where the decision maker’s interest lies

Modelling The Aids Epidemic In Malaysia

There are generally three methods of modelling the acquired immuno deficiency syndrome (AIDS) epidemic. At one extreme is the attempt to fit a function of calendar time such as a polynomial or other mathematically convenient curves to the AIDS incidence curve while the other extreme attempts to model the full dynamics of the transmission of the epidemic in the population providing much insight into the qualitative evolution of the epidemic and idenifying the key variables that determine the future number of cases

A Neural Network Approach to Synthetic Control Chart for the Process Mean

In this project, a multivariate synthetic control chart for monitoring the process mean vector of skewed populations using weighted standard deviations has been proposed. The proposed chart incorporates the weighted standard deviation (WSD) method of Chang and Bai (2004) into the standard multivariate synthetic chart of Ghute and Shirke (2008)

Bus Timetabling as a Fuzzy Multiobjective Optimization Problem Using Preference-based Genetic Algorithm

Transportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example

A Comparison On Neural Network Forecasting.

This study compares the effectiveness of the Box-Jenkins model and neural networks model in making a forecast. An eighteen years bimonthly water consumption in Penang data set is analyzed. Multilayer perceptron (MLP) in neural network with single hidden layer and double hidden layers using errorbackpropagation algorithm models are used

Statistical Methodologies Symposium celebrating The Work Of Professor Chin-diew Lai

This study briefly look at some of the statistical methods that have been developed to model the HIVIAIDS epidemic and also use the back calculation method to estimate the HIV infection rate in Penang. The back calculation program has been chosen to model the underlying HIVIAIDS epidemic in Penang, Malaysia because it makes use of the AIDS incidence data which is more reflective of the epidemic as compared to the number of HIV infected recorded which is known only if tests are conducte