Convergence of an iterative neural network learning algorithm for linearly dependent patterns

In this paper, iterative learning identification algorithms are proposed to estimate the time-varying parameters in multi-input-single-output (MISO) Wiener nonlinear time-varying systems. The regression model of the Wiener system is built by using the polynomial expansion of the nonlinear inverse function. Then, two iterative learning algorithms, including iterative learning gradient identification and iterative learning least squares identification, are presented to estimate the time-varying parameters of the regression model. The convergence performance of the iterative learning identification algorithms is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed algorithms.

This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iterations for the learning sequence is defined with maintaining the network stability. From the result of simulation experiment in terms of memory capacity and noise tolerance, the proposed model has the superior ability than the model with a complex-valued pseudo inverse learning algorithm.

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location are observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effect or position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.

In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control) which improves the robustness of the conventional ILC. The convergence of the control algorithm is also proved. Finally, simulation results for a 5-DOF manipulator have been presented to illustrate that the proposed algorithm is more robust than a first order P type ILC method at the presence of white Gaussian noise as a nonrepeating disturbance.

Aiming at the problem of a great deal of blindly searching the proportional, differential, integral parameters in the process of designing PID controller, the new algorithm combining genetic algorithm with iterative learning algorithm is named as an iterative learning genetic algorithm (ILGA), which can be used to optimize three controller parameters, thus the optimal parameters can be achieved swiftly by virtue of less iterative learning times, and the design of the PID controller is simplified. As the simulation results shown, the effectiveness of the method is verified in optimizing the PID controller parameters

In positron emission tomography (PET) imaging, the whole body reconstruction is not required if the region of interest (ROI) is only a part of the object. This paper researched a local image reconstruction algorithm based on the PI-lines, and proposed the derivative-backprojection-Hilbert (DBH) algorithm which can use the incomplete PET projection data to achieve accurate reconstruction of ROI. We designed simulation experiments to evaluate the performance of the DBH algorithm with thorax model as the object of PET image reconstruction, and compared root mean square error (RMSE) of DBH reconstruction and RMSE of filtered backprojection (FBP) reconstruction from simulations data sets with and without noise. The result shows that DBH algorithm has advantages over traditional FBP algorithm, the RMSE decreases by 49.07% and 51.19% with the coincidence events number of 20 million and 40 million, respectively. Compared with traditional maximum-likelihood expectation-maximization (ML-EM), the local iteration algorithm which uses DBH reconstruction as the initial image produces a better reconstructed image at fewer iterations with the coincidence events number of 60 million. DBH algorithm is expected to be applied to fast two-dimensional local image reconstruction and optimize the local iterative reconstruction algorithm in clinical diagnosis.

Prestasi lasak bagi skema kawalan robot sangat perlu untuk memastikan robot dapat bekerja dengan berkesan seperti yang dikehendaki dalam persekitaran terbatas melibatkan gangguan, perubahan parameter, ketidaktentuan dan kepelbagaian keadaan operasi. Kajian yang dibuat adalah berkaitan dengan satu skema kawalan daya aktif dan algoritma pembelajaran berlelaran (AFCAIL) yang melibatkan satu ciri pembaikan dalam bentuk penggunaan kriteria memberhenti yang sesuai dimuatkan dalam strategi kawalan. Skema tersebut digunapakai terhadap sistem pengolah robotik planar berlengan–dua yang beroperasi secara mendatar. Kriteria memberhenti yang dicadangkan adalah reka bentuk untuk memberhentikan proses pembelajaran berlelaran apabila syarat atau keadaan berkaitan dengan kejituan ketika melakukan tugas serta perolehan matriks inersia anggaran pengolah yang dikehendaki dapat dipenuhi. Dengan cara demikian, robot dikatakan dapat beroperasi dengan baik sebagaimana yang diarahkan. Keberkesanan skema juga dikaji dengan mengambil kira beberapa keadaan bebanan dan operasi. Kata kunci: Robot; kawalan daya aktif; algoritma pembelajaran berlelaran; kriterion memberhenti The robust performance of a robot control scheme is vital to ensure that the robot accomplishes its tasks desirably in a constraint environment involving disturbances, parametric changes, uncertainties and varied operating conditions. The study introduces the Active Force Control and Iterative Learning Algorithm (AFCAIL) scheme with an improved feature in the form of a suitably designed stopping criterion incorporated in the control strategy. The scheme is applied to the control of a horiziontally operated robotic two–link planar manipulator. The proposed stopping criterion is specifically designed to halt the iterative learning process when the conditions related to the accuracy of the performed tasks and the acquisition of appropriate estimated inertia matrix of the robot arm are favourably met. In this way, the robot is said to perform desirably and excellently. The effectiveness of the scheme is also investigated by considering a number different loading and operating conditions. Key words: Robot; active force control; iterative learning algorithm; stopping criterion

This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time $H_{\infty}$ tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

Gimbal-less dual axis point-to-point (quasistatic) MEMS mirrors have very wide bandwidths for laser beam steering, however users are often limited to only a third of the bandwidth due to the high Q-factor and use of low-pass filters in open-loop operation to avoid overshoot and oscillation. Closed-loop driving enables the use of the full bandwidth with additional complexity in optics and electronics, which can be undesirable in some low SWaP-C systems. But for many applications which require scanning repetitive patterns, such as LiDAR and biomedical imaging, bandwidth utilization and linearity can be greatly increased without any real-time feedback control by training the device and finding an optimal driving waveform using an iterative learning algorithm. The algorithm drives the device with a trial waveform, measures the scan on a Position Sensing Device (PSD), calculates the error between the desired waveform and the measured position, and adjusts the drive waveform for the next iteration based on an approximate linear device model. This is repeated until the error is reduced to below an acceptable specification. The waveform is then saved in the MEMS Controller and can be reliably used for extended periods of operation. Multiple such drive signals can be trained and stored on the controller to perform different types of scans. Several MEMS mirrors, including single- and dual-axis designs, were studied and three are reported here. Overall, in all cases a high accuracy of optical scans is achieved, typically to within ±0.025° of nominal. Repeatability after training, then running in open loop is better than ±0.01° - however, this measurement was limited by the lower resolution of the position detecting sensor. Scan rates achieved vary based on mirror design, but in each case are greatly improved from those achievable with basic driving approaches. Each mirror demonstrated higher quality vector graphics content at faster refresh rates and stable linear rasters at rates below resonance where lines are scanned with uniform velocity. Additionally, each mirror could achieve stable fast rasters with the line-scanning axis rates just below resonance, giving sinusoidal scans with line rates of ~1.6fres. Finally, each mirror was also demonstrated achieving rasters with rates above resonance, giving line rates of ~2.5fres. In all of those cases the other axis could scan linear and sharp sawtooth or triangle waveforms. Based on the symmetry of the MEMS design, we demonstrated the same performance at ddegerent angles, e.g. rastering at a 45° angle.

Constrained Point-to-Point Iterative Learning Control

Learning fixed point patterns by recurrent networks

Geospatial data were exclusively generated by official agencies. However, following the technological revolution in data collection and production, various sources have emerged for the massive production of geospatial data, resulting the phenomenon of big geo-data. Therefore, dealing with large amounts of these data sets, results in a high velocity as they change very quickly, is a challenging task. Hence, analysis become more complex and computation become prohibitively expensive. As a result, spatial computing technologies become limited in front of these complex data and operations. Accordingly, we aimed to refine complexity with simplicity by replacing traditional geospatial models with referring to the simplest intelligent and minimum resource requirement algorithms that can be applied against these constraints, while ensuring the criteria of performance and scalability. In this work, we focus on the high-velocity of this big geo-data through the use of an iterative approach applied to a feature learning algorithms to decrease the memory consumption and the time complexity of traditional machine learning algorithms. According to our knowledge, although they were widely applied in the 19th century as a solution to overcome the problems of limitation of memory and computing resources. Iterative methods were still not used for the big geo-data analytics and generally for the big data domain. Thus, this approach could be beneficial especially for real time applications such as the anomaly monitoring and detection.

Iterative trajectory learning for highly accurate optical satellite tracking systems

In this work, we present a new machine learning-based framework for the accurate estimation of the shot-noise limited photon-counting Poisson channel by developing a state-of-art iterative unsupervised learning algorithm for intelligent optical communications. By accurate estimation, we mean that it achieves very low estimation error even when the effect of the received data is unpredictable. We consider a realistic situation where modulated symbols are assumed to be hidden or unobserved latent variables, thereby making conventional estimation algorithms based on maximum likelihood approach unsuitable or inefficient. In particular, we consider a probabilistic model and assume that the received data is not labeled. With this unpredictable data considered, a novel iterative machine learning framework is developed based on an expectation and maximization algorithm. The proposed algorithm avoids the need to choose an appropriate step size as required in gradient method based algorithms. It is shown that it significantly outperforms the least square and the Viterbi detection technique.