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Open AccessJournal ArticleDOI

Fully Automated Design of Super-High-Rise Building Structures by a Hybrid AI Model on a Massively Parallel Machine

Hojjat Adeli, +1 more
- 15 Mar 1996 - 
- Vol. 17, Iss: 3, pp 87-93
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TLDR
This article presents an innovative research project where computationally elegant algorithms based on the integration of a novel connectionist computing model, mathematical optimization, and a massively parallel computer architecture are used to automate the complex process of engineering design.
Abstract
This article presents an innovative research project (sponsored by the National Science Foundation, the American Iron and Steel Institute, and the American Institute of Steel Construction) where computationally elegant algorithms based on the integration of a novel connectionist computing model, mathematical optimization, and a massively parallel computer architecture are used to automate the complex process of engineering design.

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Citations
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Journal ArticleDOI

Improved spiking neural networks for EEG classification and epilepsy and seizure detection

TL;DR: It is concluded that RProp is the best training algorithm because it has the highest classification accuracy among all training algorithms specially for large size training datasets with about the same computational efficiency provided by SpikeProp.
Journal ArticleDOI

Enhanced probabilistic neural network with local decision circles: A robust classifier

TL;DR: An enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming of PNN and improve its robustness to noise in the data.
Journal ArticleDOI

A probabilistic neural network for earthquake magnitude prediction

TL;DR: The PNN model presented in this paper complements the recurrent neural network model developed by the authors previously, where good results were reported for predicting earthquakes with magnitude greater than 6.0.
Journal ArticleDOI

Recurrent Neural Network for Approximate Earthquake Time and Location Prediction Using Multiple Seismicity Indicators

TL;DR: This paper presents a computational approach for predicting the location and time of occurrence of future moderate-to-large earthquakes in an approximate sense based on neural network modeling and using a vector of 8 seismicity indicators as input.
Journal ArticleDOI

Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence.

TL;DR: It is shown that extracting features from EEG sub-bands using coherence, as a measure of cortical connectivity, can discriminate AD patients from healthy controls effectively when a mixed band classification model is applied.
References
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Book

Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems

TL;DR: Perceptron Learning with a Hidden Layer and an Object-Oriented Backpropagation Learning Model and Adaptive Conjugate Gradient Learning Algorithm for Efficient Training of Neural Networks.
Book

Advances in Design Optimization

Hojjat Adeli
TL;DR: In this article, the authors summarized advances in a number of fundamental areas of optimization with application in engineering design, including the selection of the "best" or "optimum" design.
Journal ArticleDOI

A neural dynamics model for structural optimization—Theory

TL;DR: In this paper, a neural dynamics model is presented for optimal design of structures, which consists of two distinct layers: a variable layer and a constraint layer, where the number of nodes in the variable and constraint layers correspond to the numbers of design variables and constraints in the structural optimization problem.
Journal ArticleDOI

Perceptron Learning in Engineering Design

TL;DR: A model of machine learning in engineering design is presented based on the concept of self-adjustment of internal control parameters and perceptron, and a comparison of perceptron and explanation-based learning is concluded.
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