Unorganized Machines

2011 ◽  
Vol 2 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

Unorganized Machines

2014 ◽  
pp. 221-236 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

scholarly journals Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8096
Author(s):  
Paulo S. G. de Mattos Neto ◽  
João F. L. de Oliveira ◽  
Priscilla Bassetto ◽  
Hugo Valadares Siqueira ◽  
Luciano Barbosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Performance Metrics ◽  
Extreme Learning Machines ◽  
Statistical Machine Learning ◽  
Smart Meters ◽  
Learning Machines ◽  
Echo State Networks ◽  
Artificial Neural

The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.

UNORGANIZED MACHINES FOR SEASONAL STREAMFLOW SERIES FORECASTING

2014 ◽  
Vol 24 (03) ◽  
pp. 1430009 ◽  
Author(s):  
HUGO SIQUEIRA ◽  
LEVY BOCCATO ◽  
ROMIS ATTUX ◽  
CHRISTIANO LYRA
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Results ◽  
Extreme Learning Machines ◽  
Processing Capability ◽  
Learning Machines ◽  
Echo State Networks ◽  
Hydroelectric Plants ◽  
Seasonal Streamflow

Modern unorganized machines — extreme learning machines and echo state networks — provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

2021 ◽  
pp. 2100027
Author(s):  
Pere Mujal ◽  
Rodrigo Martínez‐Peña ◽  
Johannes Nokkala ◽  
Jorge García‐Beni ◽  
Gian Luca Giorgi ◽  
...  
Keyword(s):  
Reservoir Computing ◽  
Extreme Learning Machines ◽  
Learning Machines

Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks

2015 ◽  
Vol 744-746 ◽  
pp. 1938-1942
Author(s):  
Yi He ◽  
Duan Feng Chu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comfort Level ◽  
Subjective Rating ◽  
Acceleration Data ◽  
Acceleration And Deceleration ◽  
The Subject ◽  
Artificial Neural ◽  
The Relationship ◽  
Subjective Sensation

As the siginificant factors influence passengers comfort, the vehicle celebration performance may easy to cause accidents, such as hard acceleration and deceleration performance. In order to find the relationship between passengers comfort and celebration performance, 35 passengers and three professional drivers were recruited in the field experiment. The passengers’ comfort feelings were analysed by subject questionnaires, the acceleration and deceleration data were received by CAN bus.The Artificial Neural Networks (ANNs) model was elaborated to estimate and predict the passengers comfort level of driver unsafe acceleration behavior situations. Therefore, the subject views of the passengers could be compared to object acceleration data. An ANN is applied to interconnect output data (subjective rating) with input data (objective parameters). Finally, it is found the investigatioin have demonstrated that the objective values are efficiently correlated with the subjective sensation. Thus, the presented approach can be effectively applied to support the drive train development of bus.

Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks

2010 ◽  
Vol 102-104 ◽  
pp. 846-850
Author(s):  
Wen Yu Pu ◽  
Yan Nian Rui ◽  
Lian Sheng Zhao ◽  
Chun Yan Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Selection System ◽  
Process Data ◽  
Replacement Strategy ◽  
Machining Quality ◽  
Main Factors ◽  
Artificial Neural ◽  
Neural Networks Optimization ◽  
The Relationship

Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

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