Cutting forces are small, and in many cases insignificant, compared with noise during the micro-machining of many non-metals. The Neural-Network-based Periodic Tool Inspector (NPTI) is introduced to evaluate tool condition periodically on a test piece during the machining of non-metal workpieces. The cutting forces are measured when a slot is being cut on the test piece and the neural network e...

A novel temporal updating approach for probabilistic neural network classifiers was developed by Tian et al. (2000) to account for temporal changes of spectral and temperature features of clouds in the visible and infrared GOES 8 (Geostationary Operational Environmental Satellite) imagery data. In this paper, a new method referred to as moving singular value decomposition (MSVD) is introduced t...

Robustness of deep neural networks is a critical issue in practical applications. In the general case feed-forward (including convolutional network architectures), under random noise attacks, we propose to study probability that output deviates from its nominal value by given threshold. We derive simple concentration inequality for propagation input uncertainty through using Cramer–Chernoff met...

Probabilistic inference networks capture the stochastic relation between variables by ‘directed’ probabilistic rules corresponding to conditional probabilities, e.g. p(Ak|Ai∧Aj). Associative neural networks – like Boltzmann machine networks – yield a joint distribution, which is a special case of the distribution generated by inference networks. In this paper conventional associative neural net...

Perineuronal nets (PNNs) are extracellular molecules that form around neurons near the end of critical periods during development. They surround neuronal cell bodies and proximal dendrites. PNNs inhibit the formation of new connections and may concentrate around rapidly firing inhibitory interneurons. Previous work characterized the important role of perineuronal nets in plasticity in the visua...

Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. This paper presents a probabilistic neural network (PNN) and new feature selection technique for fault classification in transmission lines. Initially, wavelet transform is used for feature extraction from half cycle of post-fa...