Pusat Pengajian Sains Matematik - Tesis

Browse

Now showing 1 - 5 of 434

Y-type Random 2-satisfiability In Discrete Hopfield Neural Network
(2024-09)
Guo, Yueling
In the current development of Artificial Intelligence, Satisfiability plays a crucial role as a symbolic language of Artificial Intelligence for the transparency of black box models. However, the main problem of existing Satisfiability is the lack of combined logical rule, so the benefits of combined logical rule have not yet been investigated. The rule namely Y-Type Random 2-Satisfiability is proposed by combining the systematic and non-systematic logical rule. Next, the newly proposed logical rule as the symbolic instruction was implemented into the Discrete Hopfield Neural Network to govern the neurons of the network. Experimental results demonstrated the compatibility of the proposed logical rule and the Discrete Hopfield Neural Network. Additionally, the proposed Hybrid Differential Evolution Algorithm was implemented into the training phase to ensure that the cost function of the Discrete Hopfield Neural Network is minimized. During the retrieval phase, a new activation function and Swarm Mutation were proposed to ensure the diversity of the neuron states. The proposed algorithm and mutation mechanism showed optimal performances as compared to the existing algorithms. Finally, a new logic mining model namely Y-Type Random 2-Satisfiability Reverse Analysis was proposed, which showed optimal performances in terms of several metrics as compared to the existing classification models. The developed logic mining will be used to analyze the Alzheimer's Disease Neuroimaging Initiative dataset.
Visualization Of Curve And Surface Data Using Rational Cubic Ball Functions
(2018-02)
Wan Jaafar, Wan Nurhadani
This study considered the problem of shape preserving interpolation through regular data using rational cubic Ball which is an alternative scheme for rational Bezier functions. A rational Ball function with shape parameters is easy to implement because of its less degree terms at the end polynomial compared to rational Bezier functions. In order to understand the behavior of shape parameters (weights), we need to discuss shape control analysis which can be used to modify the shape of a curve, locally and globally. This issue has been discovered and brought to the study of conversion between Ball and Bezier curve. A conversion formula was obtained after a Bezier curve converted to the generalized form of Ball curve. It proved that this formulae not only valuable for geometric properties studies but also improves on the computational speed of the Ball curves.
Optimization Methods In Training Neural Networks
(2003-07)
Sathasivam, Saratha
In steepest descent methods, we construct a functional which when extremized will deliver a solution. The function will have convexity properties, so that the vector that extremizes the function is the solution of the algebraic problem in question. This means the search for the vector for which the gradient of the function is zero can be done in an iterative fashion. A special steepest descent method which is appropriate for the solution of the linear algebraic problem is the 'Conjugate Gradient Method'.
Numerical Integration And Data Reduction For Application In Photometry
(2003-10)
Chew, Tong Fatt
In this thesis, an algorithm based on a local interpolation scheme called the Average Angle Method was developed for numerical integration and data reduction of photometric data, and in general, for any data set sharing similar characteristics as the luminous intensity data The new interpolation scheme interpolates discrete data with piecewise cubic polynomials having a CI continuity in the interior. knots. The first derivatives at the interior knots are estimated from the average of the angles subtended to the horizontal by two adjacent slopes. In a further development, the algorithm is extended to create another algorithm using quintic polynomial interpolation that has C2 continuity, providing smoother interpolation curves but retaining the local nature of interpolation.
MAA 161 - Statistics for Science Students
(2022-08)
PPSM, Pusat Pengajian Sains Matematik
Second Semester Examination 2021/2022 Academic Session August 2022 MAA 161 - Statistics for Science Students Duration: 2 hours

Browse

Recent Submissions