Publications related to A feedback analysis of perceptron learning for neural networks

Adaptive hot water production based on Supervised Learning

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2020

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Mohsen Talebkeikhah, Farzaneh Talebkeikhah

Transformer oil-based nanofluids are known to have higher thermal conductivity and heat transfer performance compared to conventional transformer oils. In this study, four different types of transformer oil-based nanofluids are synthesized using the well-k ...

ELSEVIER2020

Detection of Bad PMU Data using Machine Learning Techniques

Alexandra Cameron Karpilow

This project presents a pre-State Estimation method for the detection of Bad Data (BD) in Phasor Measurement Units (PMUs) using correlation analysis and a Neural Network Classifier. Presented in this paper is the algorithm design, the steps for generating ...

IEEE2020

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Mohsen Talebkeikhah, Farzaneh Talebkeikhah

This study evaluates and compares several machine learning methods on the effects of different parameters in the hydrothermal carbonisation (HTC) process of macroalgae Sargassum horneri. Reaction temperature, residence time, biomass particle size, the amou ...

TAYLOR & FRANCIS LTD2019

Incremental Learning Meets Reduced Precision Networks

Hardware accelerators for Deep Neural Networks (DNNs) that use reduced precision parameters are more energy efficient than the equivalent full precision networks. While many studies have focused on reduced precision training methods for supervised networks ...

2019

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Charlotte Marie Bürki

Air pollutants emissions in urban areas are numerous and heterogeneous. Traditional monitoring techniques are restricted to a few highquality instruments missing this spatial heterogeneity. Recently, great interest has been given to low-cost sensors to bet ...

2017

Learning Network Structures from Firing Patterns

Martin Vetterli, Amin Karbasi, Amir Hesam Salavati

How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes’ behaviors (e.g., the time ...

Ieee2016

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

Michael Herzog, Gregory Francis, Aaron Michael Clarke

Experimentalists tend to classify models of visual perception as being either local or global, and involving either feedforward or feedback processing. We argue that these distinctions are not as helpful as they might appear, and we illustrate these issues ...

Frontiers Research Foundation2014

Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Matthew Wodrich

Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...

2009

A feedback analysis of perceptron learning for neural networks

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Adaptive hot water production based on Supervised Learning

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Detection of Bad PMU Data using Machine Learning Techniques

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

Incremental Learning Meets Reduced Precision Networks

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Learning Network Structures from Firing Patterns

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

Network Simplification: the Gaussian N-Relay Diamond Network

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Incremental Learning Meets Reduced Precision Networks

Network Simplification: the Gaussian N-Relay Diamond Network

In Situ Calibration of Low-cost Air Quality Sensors using Artificial Neural Network

Adaptive hot water production based on Supervised Learning

Detection of Bad PMU Data using Machine Learning Techniques

Physico-chemical properties prediction of hydrochar in macroalgae Sargassum horneri hydrothermal carbonisation

On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study

Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception

Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Learning Network Structures from Firing Patterns