Abstract: This study presents a hybrid neural network and Gravitational Search Algorithm (HNGSA) method to solve well known Wessinger's equation. To aim this purpose, gravitational search algorithm (GSA) technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Wessinger's equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfies the initial/ boundary conditions and does not contain any adjustable parameters and the second part which is constructed so as not to affect the initial/boundary conditions. The second part involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. In order to demonstrate the presented method, the obtained results of the proposed method are compared with some known numerical methods. The given results show that presented method can introduce a closer form to the analytic solution than other numerical methods. Present method can be easily extended to solve a wide range of problems.
Abstract: Knowing consumers' preferences and perceptions of
the sensory evaluation of drink products are very significant to
manufacturers and retailers alike. With no appropriate sensory
analysis, there is a high risk of market disappointment. This paper
aims to rank the selected coffee products and also to determine the
best of quality attribute through sensory evaluation using fuzzy
decision making model. Three products of coffee drinks were used
for sensory evaluation. Data were collected from thirty judges at a
hypermarket in Kuala Terengganu, Malaysia. The judges were asked
to specify their sensory evaluation in linguistic terms of the quality
attributes of colour, smell, taste and mouth feel for each product and
also the weight of each quality attribute. Five fuzzy linguistic terms
represent the quality attributes were introduced prior analysing. The
judgment membership function and the weights were compared to
rank the products and also to determine the best quality attribute. The
product of Indoc was judged as the first in ranking and 'taste' as the
best quality attribute. These implicate the importance of sensory
evaluation in identifying consumers- preferences and also the
competency of fuzzy approach in decision making.
Abstract: An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Abstract: Automatic methods of detecting changes through
satellite imaging are the object of growing interest, especially
beca²use of numerous applications linked to analysis of the Earth’s
surface or the environment (monitoring vegetation, updating maps,
risk management, etc...). This work implemented spatial analysis
techniques by using images with different spatial and spectral
resolutions on different dates. The work was based on the principle
of control charts in order to set the upper and lower limits beyond
which a change would be noted. Later, the a contrario approach was
used. This was done by testing different thresholds for which the
difference calculated between two pixels was significant. Finally,
labeled images were considered, giving a particularly low difference
which meant that the number of “false changes” could be estimated
according to a given limit.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: This paper presents the use of anti-sway angle control
approaches for a two-dimensional gantry crane with disturbances
effect in the dynamic system. Delayed feedback signal (DFS) and
proportional-derivative (PD)-type fuzzy logic controller are the
techniques used in this investigation to actively control the sway
angle of the rope of gantry crane system. A nonlinear overhead
gantry crane system is considered and the dynamic model of the
system is derived using the Euler-Lagrange formulation. A complete
analysis of simulation results for each technique is presented in time
domain and frequency domain respectively. Performances of both
controllers are examined in terms of sway angle suppression and
disturbances cancellation. Finally, a comparative assessment of the
impact of each controller on the system performance is presented and
discussed.
Abstract: Design of a fixed parameter robust STATCOM controller for a multi-machine power system through an H-? based loop-shaping procedure is presented. The trial and error part of the graphical loop-shaping procedure has been eliminated by embedding a particle swarm optimization (PSO) technique in the design loop. Robust controllers were designed considering the detailed dynamics of the multi-machine system and results were compared with reduced order models. The robust strategy employing loop-shaping and PSO algorithms was observed to provide very good damping profile for a wide range of operation and for various disturbance conditions.