# Heterogeneous Multilayer Generalized Operational Perceptron

arXiv: https://arxiv.org/abs/1804.05093 · HTML: https://mlsanity.com/paper/1804.05093/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks](https://mlsanity.com/paper/2110.02215/) (2021)
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- [Global ECG Classification by Self-Operational Neural Networks with Feature Injection](https://mlsanity.com/paper/2204.03768/) (2022)
- [Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression](https://mlsanity.com/paper/2105.12107/) (2021)
- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://mlsanity.com/paper/2207.11577/) (2022)
- [Zero-Shot Motor Health Monitoring by Blind Domain Transition](https://mlsanity.com/paper/2212.06154/) (2022)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)
- [2D Self-Organized ONN Model For Handwritten Text Recognition](https://mlsanity.com/paper/2207.08139/) (2022)
- [Operational Support Estimator Networks](https://mlsanity.com/paper/2307.06065/) (2023)
- [Knowledge Distillation By Sparse Representation Matching](https://mlsanity.com/paper/2103.17012/) (2021)
- [Online Non-Destructive Moisture Content Estimation of Filter Media During Drying Using Artificial Neural Networks](https://mlsanity.com/paper/2303.15570/) (2023)

## Abstract (arXiv)

The traditional Multilayer Perceptron (MLP) using McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, Generalized Operational Perceptron (GOP) was proposed to extend conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, Progressive Operational Perceptron (POP) algorithm was proposed to optimize a pre-defined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.