Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 176 for “"Artificial neural network (ANN)"”.
-
Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners
Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and …
-
Tire-Pavement Interaction Noise (TPIN) Modeling Using Artificial Neural Network (ANN)
… on the experimental noise data collected, two artificial neural networks (ANN) were developed to predict the tread pattern (ANN1) and the non-tread pattern noise (ANN2) components, separately. The inputs of ANN1 are the coherent tread profile spectrum and the air volume velocity spectrum …
-
Classification of electroencephalogram (EEG) for lower limb movement of post stroke patients using artificial neural network (ANN)
… to user intentions. However, conventional BCI cannot be used fully, due to the lack of accuracy, and need some improvement. In addition to that, the integration of BCI with lower extremity FES systems has received less attention compared to the BCI-FES systems with upper extremity. The …
-
Exergy Based SI Engine Model Optimisation. Exergy Based Simulation and Modelling of Bi-fuel SI Engine for Optimisation of Equivalence Ratio and Ignition Time Using Artificial Neural Network (ANN) Emulation and Particle Swarm Optimisation (PSO).
… performance based upon exergy analysis. An artificial neural network (ANN) is used as an emulator to speed up the optimisation processes. Constrained particle swarm optimisation (CPSO) is employed to identify parameters such as equivalence ratio and ignition time for optimising of the engine …
-
Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS
… most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is …
-
PREDICTIONS OF SIGNIFICANT WAVE HEIGHT IN LAKE OKEECHOBEE, FLORIDA USING APPROACHES RELATED TO SIMPLIFIED STOCHASTIC PROCEDURE AND WAVE ENERGY SPECTRUM
… heights from each model are compared with the Artificial Neural Network (ANN) predictions obtained by Altunkaynak and Wang (2012). The comparisons between predicted significant wave heights from each model and observed data indicate that the proposed RM1, RM2, PLSM and MPM are effective models …
-
Machine Learning Prediction of Shear Capacity of Steel Fiber Reinforced Concrete
… suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to predict the shear strength of SFRC beams with great accuracy. Different statistical metrics were employed to assess the reliability of the proposed models. The suggested models have …
-
Evolvable Mathematical Models: A New Artificial Intelligence Paradigm
We develop a novel Artificial Intelligence paradigm to generate autonomously artificial agents as mathematical models of behaviour. Agent/environment inputs are mapped to agent outputs via equation trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations …
-
Specific bio-modeling and analysis techniques at cellular and systems level
… are developed using statistical and black box (artificial neural network, ANN) techniques.The MU 2-D Man, a human thermal model has been developed for designing an automatic thermal comfort control strategy for NASA astronaut space suits and for the US Air Force warfighters in chemo-bio suits. …
-
A Predictive Model Which Uses Descriptors of RNA Secondary Structures Derived from Graph Theory.
… structure may be represented by a graph in this manner. We define novel graphical invariants to quantify the multigraphs and obtain characteristic descriptors of the secondary structures. These descriptors are used to train an artificial neural network (ANN) to recognize the characteristics of …
-
Prediction of Bridge Fires Characteristics Using Machine Learning
… database containing 171 bridges. Using an Artificial Neural Network (ANN) model, the vulnerability of bridges in fire is estimated, and the extent of damage is determined based on several key factors including bridge proximity to urban, suburban, or rural areas, structural system, …
-
Structural Damage Assessment Using Artificial Neural Networks and Artificial Immune Systems
… the first time into SHM systems. Among them, the artificial neural network (ANN) and artificial immune systems (AIS) show great potential. In this thesis, features are extracted out of the acceleration data with the use of discrete wavelet transforms (DWT)s first. The DWT coefficients are used to …
-
Data Driven Surrogate Models for Faster SPICE Simulation of Power Supply Circuits
… supply circuits with large power distribution networks makes simulation through the industry standard Simulation Program with IC Emphasis (SPICE) often non-convergent or prohibitively expensive. The existing solution of piecewise linear (PWL) simulation addresses these issues with reasonable …
-
Minimization of number of gait trials for tripping probability tests using artificial neural networks
… of this study is to apply a novel technology, artificial neural network (ANN), to predict stabilized MTC characteristics (mean, M; standard deviation, SD; skewness, S; kurtosis, K) from relatively fewer gait trials. data of 24 subjects (age range: 19-79 years) were collected during normal …
-
Predicting the threat of death in stalking cases through Artificial Neural Network
… of death against a victim by a stalker through Artificial Intelligence and, more specifically, using an Artificial Neural Network (ANN). The thesis analyses variables impact on the learning process and the accuracy of this new system about predicting the threat of death. Publicly available and …
-
Multi-objective evolutionary neural architecture search for recurrent neural networks
Artificial neural network (ANN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of ANN architectures, and has proven to be successful in finding ANN architectures that can …
-
Detecting Student Dropouts Using Fuzzy Inferencing
… that the FIS does not perform better than an Artificial Neural Network (ANN) developed for the same purpose, but useful insights about how different student attributes relate to their retention or departure may be gained from the rules that define the fuzzy model.</p>
-
Análise de dados amostrais complexos utilizando redes neurais
The fitting of an Artificial Neural Network (ANN) considers the data coming from an simple random sample with replacement. However, in practice the selection of simple random samples for surveys is rarely used and more complex sampling schemes are used. The complex sampling schemes reflect complex …
-
An artificial neural network approach to transformer fault diagnosis
This thesis presents an artificial neural network (ANN) approach to diagnose and detect faults in oil-filled power transformers based on dissolved gas-in-oil analysis. The goal of the research is to investigate the available transformer incipient fault diagnosis methods and then develop an ANN …
-
Predicting permeability and flow capacity distribution with back-propagation artificial neural networks
… demonstrated that the new technology called Artificial Neural Network (ANN), a biologically inspired, massive parallel, distributed information processing system, is an excellent tool for permeability predictions using well log data. This technology overcomes the drawbacks caused by the …
Page 1 of 9