Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique
ABSTRACT Skin detection is an important preliminary process for subsequent feature extraction in image processing techniques. There are several techniques that are used for skin detection. In this work, the multi-layer perceptron (MLP) neural network is used. One of the important aspects of MLP is how to determine the network topology. The number of neurons in the inputs and output layers are determined by the number of available inputs and required outputs respectively. Thus, the only thing remaining is how to determine the number of neurons in the hidden layer. Therefore, we employed the coarse to fine search method to find the number of neurons. First, the number of hidden neurons is initially set using the binary search mode, HN=1, 2, 4, 8, 16, 32, 64 and 128, where HN indicates the number of hidden neurons. The 30 networks with these HN values are trained and their Mean Squared Error (MSE) is calculated. Then a sequential search, fine search, will be used in the neighbourhood of the HN that gave the lowest MSE. The selected number of neurons in the hidden layer is the lowest HN that gave the lowest MSE. The YCbCr colour space is used in this work due to its capability to separate the luminance and chrominance components explicitly. Several chrominance components are investigated.
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ABSTRACT: Project management (PM) is the application of knowledge, skills, tools and techniques to project activities in order to meet project requirements. The success of any project relies heavily on the initial estimation of all project parameters. Fuzzy logic is a soft-computing technique used to effectively solve uncertainties due to imprecise inputs to generate linguistic or quantitative outputs. This paper investigates the application of fuzzy logic as a feasible technique for improved estimation accuracy to all the tasks within the project management knowledge areas to ensure higher software project success rates.Electrical and Computer Engineering, 2004. Canadian Conference on; 06/2004
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ABSTRACT: It is now generally accepted that froth appearance is a good indicative of the flotation performance. In this paper, the relationship between the process conditions and the froth features as well as the process performance in the batch flotation of a copper sulfide ore is discussed and modeled. Flotation experiments were conducted at a wide range of operating conditions (i.e. gas flow rate, slurry solids%, frother/collector dosage and pH) and the froth features (i.e. bubble size, froth velocity, froth color and froth stability) along with the metallurgical performances (i.e. copper/mass/water recoveries and concentrate grade) were determined for each run. The relationships between the froth characteristics and performance parameters were successfully modeled using the neural networks. The performance of the developed models was evaluated by the correlation coefficient (R) and the root mean square error (RMSE). The results indicated that the copper recovery (RMSE = 2.9; R = 0.9), concentrate grade (RMSE = 1.07; R = 0.92), mass recovery (RMSE = 1.94; R = 0.94) and water recovery (RMSE = 3.07; R = 0.95) can be accurately predicted from the extracted surface froth features, which is of central importance for control purposes.Minerals Engineering 12/2014; 69:137–145. DOI:10.1016/j.mineng.2014.08.003 · 1.60 Impact Factor