A CNN-based neuromorphic model for classification and decision control

Arena, Paolo; Calı̀, Michele; Patané, Luca; Portera, Agnese; Spinosa, Angelo Giuseppe

doi:10.1007/s11071-018-4673-4

Cited by 11 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to obtain better accuracy, researchers adopt the SNN+ANN approach in gas recognition and realize 97.14% and 100% accuracy in [108] and [109] respectively. Alternatively, ANN to SNN conversion (S-ANN) techniques also improve the accuracy, which are able to obtain competitive results with ANNs in terms of accuracy and computational power [131]- [133].…”

Section: Comparison Of Snn-based Gas Recognition and Other Methodsmentioning

confidence: 99%

Gas Recognition in E-Nose System: A Review

Chen

Huo

Zhang

2022

IEEE Trans. Biomed. Circuits Syst.

View full text Add to dashboard Cite

Gas recognition is essential in an electronic nose (Enose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.

show abstract

Section: Comparison Of Snn-based Gas Recognition and Other Methodsmentioning

confidence: 99%

Gas Recognition in E-Nose System: A Review

Chen

Huo

Zhang

2022

IEEE Trans. Biomed. Circuits Syst.

View full text Add to dashboard Cite

show abstract

“…These stimuli can create the internal dynamics whose effect is to reinforce the previously learned sequences. Part of this structure was subsequently found to act as a suitable neuromorphic model for classification and decision control [270]. Starting from the experimental evidence as well as from the known architecture of the fly brain, some additional behaviors were hypothesized to be hosted, which were already found in more complex insects like bees or ants.…”

Section: Mb Modelsmentioning

confidence: 99%

Insect-Inspired Robots: Bridging Biological and Artificial Systems

Manoonpong

Patané

Xiong

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

show abstract

“…The multilayer perceptron (MLP) has been employed for chatter detection by using tool vibration data [22] and neural networks are often trained using preprocessed, rather than raw noisy data, because the extraction of well-organized features is easier. Convolutional neural network (CNN) is widely implemented to solve classification problems [23,24]. Continuous wavelet transform can also be applied to preprocess data for training CNN [25,26].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Kuo

Luan

Tseng

et al. 2021

Preprint

View full text Add to dashboard Cite

Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

show abstract

A CNN-based neuromorphic model for classification and decision control

Cited by 11 publications

References 52 publications

Gas Recognition in E-Nose System: A Review

Gas Recognition in E-Nose System: A Review

Insect-Inspired Robots: Bridging Biological and Artificial Systems

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Contact Info

Product

Resources

About