NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Recurrent neural network based approach for estimating the dynamic evolution of grinding process variables

170 p.El proceso de rectificado es ampliamente utilizado para la fabricación de componentes de precisión por arranque de viruta por sus buenos acabados y excelentes tolerancias. Así, el modelado y el control del proceso de rectificado es altamente importante para alcanzar los requisitos económicos y de precisión de los clientes. Sin embargo, los modelos analíticos desarrollados hasta ahora están lejos de poder ser implementados en la industria. Es por ello que varias investigaciones han propuesto la utilización de técnicas inteligentes para el modelado del proceso de rectificado. Sin embargo, estas propuestas a) no generalizan para nuevas muelas y b) no tienen en cuenta el desgaste de la muela, efecto esencial para un buen modelo del proceso de rectificado. Es por ello que se propone la utilización de las redes neuronales recurrentes para estimar variables del proceso de rectificado que a) sean capaces de generalizar para muelas nuevas y b) que tenga en cuenta el desgaste de la muela, es decir, que sea capaz de estimar variables del proceso de rectificado mientras la muela se va desgastando. Así, tomando como base la metodología general, se han desarrollado sensores virtuales para la medida del desgaste de la muela y la rugosidad de la pieza, dos variables esenciales del proceso de rectificado. Por otro lado, también se plantea la utilización la metodología general para estimar fuera de máquina la energía específica de rectificado que puede ayudar a seleccionar la muela y los parámetros de rectificado por adelantado. Sin embargo, una única red no es suficiente para abarcar todas las muelas y condiciones de rectificado existentes. Así, también se propone una metodología para generar redes ad-hoc seleccionando unos datos específicos de toda la base de datos. Para ello, se ha hecho uso de los algoritmos Fuzzy c-Means. Finalmente, hay que decir que los resultados obtenidos mejoran los existentes hasta ahora. Sin embargo, estos resultados no son suficientemente buenos para poder controlar el proceso. Así, se propone la utilización de las redes neuronales de impulsos. Al trabajar con impulsos, estas redes tienen inherentemente la capacidad de trabajar con datos temporales, lo que las hace adecuados para estimar valores que evolucionan con el tiempo. Sin embargo, estas redes solamente se usan para clasificación y no predicción de evoluciones temporales por la falta de métodos de codificación/decodificación de datos temporales. Así, en este trabajo se plantea una metodología para poder codificar en trenes de impulsos señales secuenciales y poder reconstruir señales secuenciales a partir de trenes de impulsos. Esto puede llevar a en un futuro poder utilizar las redes neuronales de impulsos para la predicción de secuenciales y/o temporales

Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach

Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for neural networks and bio-inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human-like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate-based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains ”left” and ”right” speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and time series in real time.Ministerio de Economía y Competitividad TEC2016-77785-

Advanced Information System for Safety-Critical Processes

The paper deals with the design and implementation of an intelligent modular information system (IMIS) for modeling and predictive decision making supervisory control of some important critical processes in a nuclear power plant (nuclear reactor) using selected soft computing methods. The developed IMIS enables monitoring critical states, safety impact analysis and prediction of dangerous situations. It also recommends the operator possibilities how to proceed to ensure safety of operations and humans and environment. The proposed complex IMIS has been tested on real data from a nuclear power plant process primarily used as supervisory information for decision making support and management of critical processes. The core of the proposed IMIS is a general nonlinear neural network mathematical model. For prediction of selected process variables an artificial neural network of multilayer perceptron type (MLP) has been used. The effective Levenberg-Marquardt method was used to train the MLP network. Testing and verification of the neural prediction model were carried out on real operating data measurements obtained from the NPP Jaslovske Bohunice

Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modeling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters; then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature

Adapting and enhancing mussels wandering optimization algorithm for supervised training of neural networks

Membangunkan kaedah latihan yang cekap untuk Rangkaian Neural (NN) dalam mencapai kejituan yang tinggi adalah satu cabaran. Tambahan pula, latihan NN masih lagi memerlukan masa yang lama. Algoritma Pengoptimuman Perayauan Kupang (MWO) ialah satu algoritma pengoptimuman metaheuristik yang baru dan telah diinspirasikan secara ekologi oleh tingkah laku pegerakan kupang. Objektif utama bagi tesis ini adalah untuk mencapai prestasi yang terbaik dalam penumpuan masa latihan dan ketepatan pengelasan untuk pengelasan corak dengan mengusulkan kaedah latihan penyeliaan yang baru untuk Rangkaian Neural Buatan (ANN) yang berasaskan penggunaan algoritma MWO. Mempertingkatkan prestasi, terutamanya dalam kejituan pengelasan yang membawa kepada perkenalan versi MWO yang telah di adaptasi; dikenali sebagai algoritma Peningkatan-MWO (E-MWO). Developing efficient training method for Neural Networks (NN) in terms of high accuracy is a challenge. In addition, training NN is still highly-time consuming. The Mussels Wandering Optimization (MWO) is a recent metaheuristic optimization algorithm inspired ecologically by mussels movement behavior. The major objective of this thesis is to achieve better performance in terms of convergence training time and classification accuracy for pattern classification by proposing new supervised training methods for Artificial Neural Networks (ANN) based on the MWO algorithm. Increasing the performance, especially in terms of classification accuracy led to an adapted version of the MWO; known as Enhanced-MWO (E-MWO) algorithm

An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms

Objectives: Electroencephalogram (EEG) signal   gives   a   viable perception about the neurological action of the human brain that aids the detection of epilepsy. The objective of this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops a computer-aided diagnosis (CAD) machine learning model which can spontaneously classify pre-ictal and ictal EEG signals. In the proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) and Efficient Particle Swarm Optimization (EPSO) are used to determine the optimum parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared to the FFA and EPSO algorithm separately, the composite (ANFIS+FFA+EPSO) optimization algorithm outperforms in all respects. The proposed technique achieved accuracy, specificity, and sensitivity of 99.87%, 98.71% and 100% respectively. Conclusion: The ANFIS-FFA-EPSO method is able to enhance the seizure detection outcomes for demand forecast in hospital

SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure

This paper presents an enhanced rank - order based learning algorithm, called SpikeTemp, for Spiking Neural Networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, and an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and thus reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark datasets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms and the results demonstrate the ability of SpikeTemp to classify different datasets after just one presentation of the training samples with comparable classification performance