# Neural Networks and Applications

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/neural-networks-and-applications/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers covers a wide range of topics related to neural networks, including backpropagation learning, self-organizing maps, radial basis function networks, deep learning, and applications such as pattern classification and function approximation. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10320 |
| Works | 156 |

## Topic papers all

Showing 15 of 156.

- [Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps](https://scholariq.org/papers/fuzzy-artmap-a-neural-network-architecture-for-incremental-supervised-learning/)
- [Regression modeling strategies : with applications to linear models, logistic and ordinal regression, and survival analysis](https://scholariq.org/papers/regression-modeling-strategies-with-applications-to-linear-models-logistic-and/)
- [Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems](https://scholariq.org/papers/seagull-optimization-algorithm-theory-and-its-applications-for-large-scale/)
- [Linear discriminant analysis: A detailed tutorial](https://scholariq.org/papers/linear-discriminant-analysis-a-detailed-tutorial/)
- [ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network](https://scholariq.org/papers/artmap-supervised-real-time-learning-and-classification-of-nonstationary-data-by/)
- [A survey of kernel and spectral methods for clustering](https://scholariq.org/papers/a-survey-of-kernel-and-spectral-methods-for-clustering/)
- [Extracting Tree-Structured Representations of Trained Networks](https://scholariq.org/papers/extracting-tree-structured-representations-of-trained-networks/)
- [Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints](https://scholariq.org/papers/neural-network-control-based-adaptive-learning-design-for-nonlinear-systems-with/)
- [Human Memory: Structures and Processes](https://scholariq.org/papers/human-memory-structures-and-processes/)
- [New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay](https://scholariq.org/papers/new-delay-dependent-stability-criteria-for-neural-networks-with-time-varying/)
- [Recent advances in convolutional neural network acceleration](https://scholariq.org/papers/recent-advances-in-convolutional-neural-network-acceleration/)
- [Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data](https://scholariq.org/papers/time-series-forecasting-with-neural-networks-a-comparative-study-using-the-air/)
- [A theoretical justification of the average reference in topographic evoked potential studies](https://scholariq.org/papers/a-theoretical-justification-of-the-average-reference-in-topographic-evoked/)
- [Exponential stability and periodic oscillatory solution in BAM networks with delays](https://scholariq.org/papers/exponential-stability-and-periodic-oscillatory-solution-in-bam-networks-with/)
- [Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis.](https://scholariq.org/papers/plots-transformations-and-regression-an-introduction-to-graphical-methods-of/)

## Topic primary papers

Showing 15 of 46.

- [Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps](https://scholariq.org/papers/fuzzy-artmap-a-neural-network-architecture-for-incremental-supervised-learning/)
- [ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network](https://scholariq.org/papers/artmap-supervised-real-time-learning-and-classification-of-nonstationary-data-by/)
- [Extracting Tree-Structured Representations of Trained Networks](https://scholariq.org/papers/extracting-tree-structured-representations-of-trained-networks/)
- [Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis.](https://scholariq.org/papers/plots-transformations-and-regression-an-introduction-to-graphical-methods-of/)
- [Multilayer feedforward networks with a nonpolynomial activation function can approximate any function](https://scholariq.org/papers/multilayer-feedforward-networks-with-a-nonpolynomial-activation-function-can/)
- [Ensembles of Learning Machines](https://scholariq.org/papers/ensembles-of-learning-machines/)
- [Using neural networks for data mining](https://scholariq.org/papers/using-neural-networks-for-data-mining/)
- [Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes Based on KL Decomposition, MLP and LSTM Network](https://scholariq.org/papers/spatiotemporal-modeling-for-nonlinear-distributed-thermal-processes-based-on-kl/)
- [Suppressing chaos in neural networks by noise](https://scholariq.org/papers/suppressing-chaos-in-neural-networks-by-noise/)
- [USING THE CORRELATION DIMENSION FOR VIBRATION FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS—I. BASIC CONCEPTS](https://scholariq.org/papers/using-the-correlation-dimension-for-vibration-fault-diagnosis-of-rolling-element/)
- [Circular backpropagation networks for classification](https://scholariq.org/papers/circular-backpropagation-networks-for-classification/)
- [Hybrid neural networks for big data classification](https://scholariq.org/papers/hybrid-neural-networks-for-big-data-classification/)
- [Incremental Learning of Chunk Data for Online Pattern Classification Systems](https://scholariq.org/papers/incremental-learning-of-chunk-data-for-online-pattern-classification-systems/)
- [Neural Information Processing](https://scholariq.org/papers/neural-information-processing/)
- [Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering](https://scholariq.org/papers/automatic-identification-of-significant-graphoelements-in-multichannel-eeg/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.