Neural networks : a classroom approach

Neural networks with a good modeling capability have been used increasingly to predict and forecast solar radiation. Even diverse application of neural network has been reported in literatures such as robotics, pattern recognition, forecasting, power systems, optimization and social/psychological sciences etc. The models have categorized the review under three major performance schemes such as delay, number of neurons and activation function for establishment of neural network architecture. In each of these categories, we summarize the major applications of eight well recognized and often used neural network models of which the last two are custom based. The anticipated model are initiated and validated with 10 metrological parameters further in sub-categories. Evaluation of its accuracy associated with special flexibility of the model is demonstrated through the results based on parameter range. In summary, we conclude the best result showing that the delays, neuron, transfer function, model, parameters and RMSE errors are in range of 15 or 30, 10 or 20, tansig, Elman Back Propagation network, bulb point temperature or direct normal radiation, 9–10 and 25–35% training to the test cases. The review discloses the incredible view of using the neural networks in solar forecast. The work of other researchers in the ﬁeld of renewable energy and other energy systems is also reported which can be used in the future in the works of this field.

Solar radiation forecasting with multiple parameters neural networks

Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).

A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

The low cost inertial navigation system (INS) suffers from bias and measurement noise, which would result in poor navigation accuracy during global positioning system (GPS) outages. Aiming to bridge GPS outages duration and enhance the navigation performance, a deep learning network architecture named GPS/INS neural network (GI-NN) is proposed in this paper to assist the INS. The GI-NN combines a convolutional neural network and a gated recurrent unit neural network to extract spatial features from inertial measurement unit (IMU) signals and track their temporal characteristics. The relationship among the attitude, specific force, angular rate and the GPS position increment is modelled, while the current and previous IMU data are used to estimate the dynamics of the vehicle by GI-NN. Numerical simulations, real field tests and public data tests are performed to evaluate the effectiveness of the proposed algorithm. Compared with the traditional machine learning algorithms, the results illustrate that the proposed method can provide more accurate and reliable navigation solutions in GPS denied environments.

Deep Learning-Enabled Fusion to Bridge GPS Outages for INS/GPS Integrated Navigation

The combined navigation system consisting of Global Positioning System (GPS) and Inertial Navigation System in a complementary mode assures an accurate, reliable, and continuous positioning capability in the navigation system. Because of problems such as dealing with a low-cost MEMS-based inertial sensors having a high level of uncertainty and imprecision, stochastic noise, a high-speed vehicle, high noisy real data, and long-term GPS signal outage during the real-time flight test, the advantage is taken for some approaches in different steps: (1) utilizing discrete wavelet transform technique to enhance the signal-to-noise ratio in raw and noisy inertial sensor signals and attenuate high-frequency noise as a preprocessing phase to prepare more accurate data for the proposed model and (2) employing adaptive neural subtractive clustering fuzzy inference system (ANSCFIS) which combines and extracts the best feature of adaptive neuro-fuzzy inference system (ANFIS), and the subtractive clustering algorithm with fewer rules than the ANFIS method, aiming to improve a more efficient, accurate, and especially a faster method which enhances the prediction accuracy and speeds up the positioning system. The achieved accuracies for the proposed model are discussed and compared with the extended Kalman filter (EKF), ANFIS, and ANSCFIS which are implemented and tested experimentally using a high-speed vehicle in three GPS blockages. The proposed model shows considerable improvements in high-speed navigation using low-cost MEMS-based inertial sensors in case of long-term GPS blockage.

Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system

An aircraft system mainly relies on a Global Positioning System GPS to provide accurate position values consistently. However, GPS receivers may encounter frequent GPS absence because of ephemeric error, satellite clock error, multipath error, and signal jamming. To overcome these drawbacks, generally a GPS is integrated with an Inertial Navigation System INS mounted inside the vehicle to provide a reliable navigation solution. INS and GPS are commonly integrated using a Kalman filter KF to provide a robust navigation solution. In the KF approach, the error models of both INS and GPS are required; this leads to the complexity of the system. This research work presents new position update architecture NPUA which consists of various artificial intelligence neural networks AINN that integrate both GPS and INS to overcome the drawbacks of the Kalman filter. The various AINNs that include both static and dynamic networks described for the system are radial basis function neural network RBFNN, backpropagation neural network BPN, forward-only counter propagation neural network FCPN, full counter propagation neural network Full CPN, adaptive resonance theory-counter propagation neural network ART-CPN, constructive neural network CNN, higher-order neural networks HONN, and input-delayed neural networks IDNN to predict the INS position error during GPS absence, resulting in different performances. The performances of the different AINNs are analyzed in terms of root mean square error RMSE, performance index PI, number of epochs, and execution time ET.

Performance analysis of various artificial intelligent neural networks for gps/ins integration

In the field of navigation, two system depends for the navigation is Inertial Navigation System (INS) and Global Positioning System (GPS). INS is an autonomous, standalone system which is available in the aircraft which gives the latitude, longitude and altitude position of the aircraft by the accelerometer and gyroscope measurements. GPS is used to provide accurate position information for the navigation. Both the system having its own drawbacks like bias error and drift error of accelerometer and gyroscope and satellite clock errors and multipath reflection errors of GPS. In order to overcome its drawback, both the systems are integrated to provide reliable navigation solution. Typically Kalman Filter (KF) is used to integrate the system. In recent years, AI (Artificial Intelligence) based systems are used for the same. In this paper, the hybrid approach of using Input delayed Dynamic Neural Network (IDNN) and KF is introduced for GPS/INS integration.

A hybrid approach for GPS/INS integration using Kalman filter and IDNN

Mental health issues are one of the major problems in medical industries and it require an effective investigation for the recovery. Machine learning plays an important role which can be applied in mental health problems. The proposed research work describes about the ECG processing using some framework for mental health diagnostic systems. The proposed work helps to detect the values of significant ECG parameters with informative values as effective and high accuracy data predicting using the parameters as time, frequency and energy from signal of ECG. This research paper implemented with spectral clustering and radial basis function network algorithm and performed well by executing with higher accuracy of value in prediction of mental health data about the patient using machine learning approaches and techniques.

Machine Learning based Patient Mental Health Prediction using Spectral Clustering and RBFN Algorithms

/pdf/performance-analysis-of-various-artificial-intelligent-4hegajhg90.pdf

Performance Analysis of Various Artificial Intelligent Neural Networks for GPS/INS Integration

This Facial recognition is a broad category of technologies used largely for surveillance. Now a days universities and multinational companies have already begun to use these incredibly important technologies to track attendance and identify individuals. The primary goal of a facial recognition system is to confirm a person’s identity by using a training database. A large number of training samples are required by the facial recognition system, which must be maintained in every storage site. As a result, storing facial photos on the blockchain creates a secure platform that is impervious to data breaches and cyber-attacks while also ensuring data availability. The facial recognition software system and the blockchain function are discussed, and a real-time facial recognition system has been built. Blockchain is also utilised to manage databases in an eHealth data management system. Patient facial data is saved on a distributed server in a secure manner.

Human Face Detection and Recognition in eHealth Implications for Blockchain Data Theory

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