Modifikasi Algoritma Propagasi Balik untuk Pengenalan Data Iris dan Data Feret

Variasi dari algoritma propagasi balik konvensional pada pelatihan jaringan saraf tiruan telah diajukan dengan menggunakan fungsi kesalahan kuadratis. Algoritma ini seperti algoritma successive overrelaxation (SOR) pada sistem persamaan linear untuk mencari nilai bobot terakhir yang digunakan untuk memperbaharui bobot pada setiap busur. Dari analisis terhadap percobaan terhadap data feret dan iris sebagai pembanding, jumlah epoch yang diperlukan untuk mencapai konvergensi dengan menggunakan BP SOR 0.9 kali lebih kecil dibandingkan dengan menggunakan BP standar. Hal ini berarti hasil yang didapatkan masih lebih besar dari treshold yang diharapkan yaitu dibawah 0.67 untuk klaim bahwa BP SOR menghasilkan waktu komputasi yang lebih cepat daripada BP. Hasil rate recognition dengan menggunakan BP SOR relatif sama dibandingkan dengan menggunakan BP standar

An Algorithm to Reconstruct the Missing Values for Diagnosing the Breast Cancer

The treatment of incomplete data is an important step in pre-processing data prior to later analysis. The main objective of this paper is to show how various methods can be used in such a way that they are able to process dataset with missing values. Computer–aided classification of Breast cancer using Back propagation neural network is discussed in this paper. The classification results have indicated that the network gave the good diagnostic performance of 99.06%

Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

Trajectory prediction of moving objects by means of neural networks

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 1997Includes bibliographical references (leaves: 103-105)Text in English; Abstract: Turkish and Englishviii, 105 leavesEstimating the three-dimensional motion of an object from a sequence of object positions and orientation is of significant importance in variety of applications in control and robotics. For instance, autonomous navigation, manipulation, servo, tracking, planning and surveillance needs prediction of motion parameters. Although "motion estimation" is an old problem (the formulations date back to the beginning of the century), only recently scientists have provided with the tools from nonlinear system estimation theory to solve this problem eural Networks are the ones which have recently been used in many nonlinear dynamic system parameter estimation context. The approximating ability of the neural network is used to identifY the relation between system variables and parameters of a dynamic system. The position, velocity and acceleration of the object are estimated by several neural networks using the II most recent measurements of the object coordinates as input to the system Several neural network topologies with different configurations are introduced and utilized in the solution of the problem. Training schemes for each configuration are given in detail. Simulation results for prediction of motion having different characteristics via different architectures with alternative configurations are presented comparatively

Application and comparison of different classification methods based on symptom analysis with traditional classification technique for breast cancer diagnosis

Novel approach for classification technique such as Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA) and Random Forest (RF) using factor or dichotomic variables has been introduced. This study searches for the highly informative finitely linear combinations (symptoms) of variables in the finite field on the based of the Fisher’s exact test and accurately predict the target class for each case in the data. There are several super symptoms have comparable p-values. In this case, it becomes possible to choose as a nominative representative the factor which is more accessible for interpretation. The super symptom means a linear combination of various multiplications of k dichotomous variables over a field of characteristic 2 without repeating. In algebra, such functions are called Zhegalkin polynomials or algebraic normal forms

Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of $|d_n| < 2.9 \times 10^{-26}\,e\,$cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature $\sim 0.5\,$K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of $0.1\,$pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of $> 1 \times 10^{7}$, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the $0.1\,$pT requirement under certain conditions

An intelligent vehicle security system based on human behaviors modeling.

by Meng Xiaoning.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 99-106).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Overview --- p.2Chapter 1.3 --- Organization of the Thesis --- p.3Chapter 2 --- Related Research --- p.6Chapter 2.1 --- Information Technology in Cars --- p.6Chapter 2.2 --- Anti-theft Protection --- p.8Chapter 2.3 --- Learning Human Behaviors --- p.10Chapter 2.4 --- Neural Network Learning --- p.11Chapter 3 --- Experimental Design --- p.14Chapter 3.1 --- Overview --- p.14Chapter 3.2 --- Driving Simulation Subsystem --- p.14Chapter 3.3 --- Data Sensing and Capturing Subsystem --- p.15Chapter 3.4 --- Data Analysis Subsystem --- p.17Chapter 4 --- Data Preprocessing for Feature Selection --- p.23Chapter 4.1 --- Introduction --- p.23Chapter 4.2 --- Fast Fourier Transform --- p.23Chapter 4.3 --- Principal Component Analysis --- p.24Chapter 4.4 --- Independent Component Analysis --- p.26Chapter 5 --- Classification via Support Vector Machine --- p.28Chapter 5.1 --- Introduction --- p.28Chapter 5.1.1 --- Why Using Support Vector Machine --- p.28Chapter 5.1.2 --- Mathematic Description --- p.29Chapter 5.2 --- Problem Formulation --- p.31Chapter 5.3 --- Approach --- p.31Chapter 5.4 --- Experimental Results --- p.34Chapter 5.4.1 --- Preprocess Data Analysis --- p.34Chapter 5.4.2 --- Models Design --- p.37Chapter 5.5 --- Discussion --- p.44Chapter 6 --- Evaluation via Hidden Markov Model --- p.47Chapter 6.1 --- Introduction --- p.47Chapter 6.1.1 --- Why Using Hidden Markov Model --- p.48Chapter 6.1.2 --- Mathematic Description --- p.50Chapter 6.2 --- Problem Formulation --- p.51Chapter 6.3 --- Approach --- p.53Chapter 6.4 --- Experimental Results --- p.56Chapter 6.4.1 --- Model-to-model Measure --- p.56Chapter 6.4.2 --- Human-to-model Measure --- p.63Chapter 6.4.3 --- Parameters Optimization --- p.66Chapter 6.5 --- Discussion --- p.69Chapter 7 --- System Design and Implementation --- p.71Chapter 7.1 --- Introduction --- p.71Chapter 7.2 --- Hardware --- p.72Chapter 7.3 --- Software --- p.78Chapter 7.4 --- System Demonstration --- p.80Chapter 8 --- Conclusion and Future Work --- p.82Chapter 8.1 --- Contributions --- p.82Chapter 8.2 --- Future Work --- p.84Chapter A --- Hidden Markov Model Training --- p.87Chapter A.1 --- Forward-backward Algorithm --- p.87Chapter A.2 --- Baum-Welch Algorithm --- p.87Chapter B --- Human Driving Behavior Data --- p.90Chapter C --- Publications Resulted from the Study --- p.9

Investigation of hidden multipolar spin order in frustrated magnets using interpretable machine learning techniques

Frustration gives rise to a plethora of intricate phenomena, the most salient of which are spin liquids, both classical ones—such as the spin-ice phase which has been realized experimentally in rare-earth oxide pyrochlore materials—and their more elusive quantum counterparts. At low temperatures, classical frustrated spin systems may still order, despite their extensive ground-state degeneracy, due to the order-by-disorder phenomenon. The resulting orders are often of a multipolar type which defies conventional probes. Identifying and characterizing such “hidden” orders is thus a challenging endeavor. This thesis introduces a machine-learning framework for studying the phase diagram of classical frustrated spin models in an unbiased and automated way. The interpretability of the resulting classification was of paramount importance in the design of the method. It allows for the inference of both the order parameter tensors of the phases with broken symmetries as well as the constraints which are characteristic of classical spin liquids and signal their emergent gauge structure. On top of that, it establishes a hierarchical relationship among the various phases according to their degree of disorder. The framework is applied to three different models and spin configurations are harvested from classical Monte Carlo simulations of those. A gauge model is used to mimic the interactions between the mesogens of generalized nematics. These may possess arbitrary point group symmetry, resulting in benchmark models with a low-temperature phase that breaks the O(3) spin symmetry accordingly. In addition, two frustrated spin models are considered. The historically important case of the Heisenberg model on the kagome lattice gives rise to hidden triatic order which requires a description in terms of two tensors of different ranks; the machine is capable of finding both. Meanwhile, for the XXZ model on the pyrochlore lattice, the machine reconstructs the complex phase diagram which was only recently obtained and correctly identifies the spin nematic phase as well as three distinct types of classical spin liquids, including their crossovers. The method has the potential to accelerate the characterization of model Hamiltonians of frustrated magnets. It can scrutinize the whole parameter space at once and may thus help to identify interesting regimes, paving the way for the search of new orders and spin liquids.Frustration führt zu einer Fülle komplexer Phänomene, von denen die herausragendsten Spinflüssigkeiten sind, sowohl klassische – wie beispielsweise die Spin-Eis-Phase, die experimentell in den Oxiden seltener Erden auf dem Pyrochlor-Gitter realisiert wurde – und ihre schwerer fassbaren quantenmechanischen Gegenstücke. Bei niedrigen Temperaturen können klassische frustrierte Spinsysteme obgleich der extensiven Entartung des Grundzustandes aufgrund des Phänomens der „Ordnung durch Unordnung“ dennoch Ordnungen ausbilden. Diese sind oft multipolarer Natur und entziehen sich herkömmlichen Messgrößen. Die Identifikation und Charakterisierung solcher „verborgener“ Ordnungen ist daher eine herausfordernde Aufgabe. In dieser Arbeit wird ein Verfahren für das unvoreingenommene und automatisierte maschinelle Lernen der Phasendiagramme klassischer frustrierter Spinmodelle eingeführt. Die Interpretierbarkeit der resultierenden Klassifikatoren war für das Design der Methode ausschlaggebend. Sie erlaubt den Rückschluss sowohl auf die Ordnungsparametertensoren der symmetriebrechenden Phasen als auch auf die Nebenbedingungen, die für klassische Spinflüssigkeiten charakteristisch sind und auf deren emergente Eichstruktur hindeuten. Darüber hinaus wird eine hierarchische Beziehung zwischen den verschiedenen Phasen gemäß dem Grade ihrer jeweiligen Unordnung hergestellt. Das Verfahren wird auf drei verschiedene Modelle angewendet und Spin-Konfigurationen werden jeweils aus klassischen Monte-Carlo-Simulationen dieser gewonnen. Ein Eichmodell dient dazu, die Wechselwirkungen zwischen den Mesogenen verallgemeinerter nematischer Flüssigkristalle nachzuahmen. Diese können beliebige Punktgruppensymmetrien besitzen, was zu Benchmark-Modellen mit einer Niedertemperaturphase führt, die die O(3)-Spinsymmetrie entsprechend herunterbricht. Darüber hinaus werden zwei frustrierte Spinmodelle betrachtet. Der historisch wichtige Fall des Heisenberg-Modells auf dem Kagome-Gitter führt zu einer verborgenen trigonalen Ordnung, die eine Beschreibung in Form von zwei Tensoren unterschiedlichen Ranges erforderlich macht; die Maschine ist in der Lage, beide zu finden. Währenddessen rekonstruiert die Maschine für das XXZ-Modell auf dem Pyrochlor-Gitter das komplexe Phasendiagramm, das erst vor Kurzem ausgearbeitet wurde, und identifiziert die spin-nematische Phase sowie drei verschiedene Arten klassischer Spinflüssigkeiten, einschließlich ihrer Übergänge, korrekt. Die Methode hat das Potenzial, die Charakterisierung von Spinmodellen frustrierter Magnete zu beschleunigen. Sie kann den gesamten Parameterraum auf einmal untersuchen und somit dazu beitragen, interessante Bereiche zu identifizieren. Dies bereitet den Weg für die Suche nach neuen Ordnungen und Spinflüssigkeiten