Jiri Bila

Adaptive Evaluation of Complex Dynamical Systems Using Low-Dimensional Neural Architectures

New methodology of adaptive monitoring and evaluation of complicated dynamic data is introduced. The major objectives are monitoring and evaluation of both instantaneous and long-term attributes of complex dynamic behavior, such as of chaotic systems and real-world dynamical systems. In the sense of monitoring, the methodology introduces a novel approach to quantification and visualization of cognitively observed system behavior in a real time without further processing of these observations. In the sense of evaluation, the methodology opens new possibilities for consequent qualitative and quantitative processing of cognitively monitored system behavior. Techniques and enhancements are introduced to improve the stability of low-dimensional neural architectures and to improve their capability in approximating nonlinear dynamical systems that behave complex in high-dimensional state space. Low-dimensional dynamic quadratic neural units enhanced as forced dynamic oscillators are introduced to improve the approximation quality of higher dimensional systems. However, the introduced methodology can be universally used for adaptive evaluation of dynamic behavior variability also with other neural architectures and adaptive models, and it can be used for theoretical chaotic systems as well as for real-word dynamical systems. Simulation results on applications to deterministic, however, highly chaotic time series are shown to explain the new methodology and to demonstrate its capability in sensitive and instantaneous detections of changing behavior, and these detections serve for monitoring and evaluating the level of determinism (predictability) in complex signals. Results of this new methodology are shown also for real-world data, and its limitations are discussed.

Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units

The paper introduces basic types of nonconventional artificial neural units and focuses their notation and classification: namely; the notation and classification of dynamic higher-order nonlinear neural units, time-delay dynamic neural units, and time-delay higher-order nonlinear neural units is introduced. Brief introduction into the simplified parallel of higher-order nonlinear aggregating function of artificial nonconventional neural units and synaptic and somatic operation of biological neurons is made. Based on still simplified mathematical notation, it is proposed that nonlinear aggregating function of neural inputs should be understood as composition of synaptic as well as partial somatic neural operation also for static neural units. Thus it unravels novel, simplified, yet universal insight into understanding more computationally powerful neurons. The classification of nonconventional artificial neural units is founded first according to nonlinearity of aggregating function, second according to the dynamic order, third according to time-delay implementation within neural units.

Quadratic neural unit and its network in validation of process data of steam turbine loop and energetic boiler

This paper discusses results and advantages of the application of quadratic neural units and novel quadratic neural network to modeling of real data for purposes of validation of measured data in energetic processes. A feed forward network of quadratic neural units (a class of higher order neural network) with sequential learning is presented. This quadratic network with this learning technique reduces computational time for models with large number of inputs, sustains optimization convexity of a quadratic model, and also displays sufficient nonlinear approximation capability for the real processes. A comparison of performances of the quadratic neural units, quadratic neural networks, and the use of common multilayer feed forward neural networks all trained by Levenberg-Marquard algorithm is discussed.

Foundations of Nonconventional Neural Units and their Classification

This article introduces basic types of nonconventional neural units and focuses on their notation and classification. Namely, the notation and classification of higher order nonlinear neural units, time-delay dynamic neural units, and time-delay higher order nonlinear neural units are introduced. Brief introduction into the simplified parallels of the higher order nonlinear aggregating function of higher order neural units with both the synaptic and somatic neural operation of biological neurons is made. Based on the mathematical notation of neural input intercorrelations of higher order neural units, it is shown that the higher order polynomial aggregating function of neural inputs can be understood as a single-equation representation of synaptic neural operation plus partial somatic neural operation. Thus, it unravels new simplified yet universal mathematical insight into understanding the higher computational power of neurons that also conforms to biological neuronal morphology. The classification of nonconventional neural units is founded first according to the nonlinearity of the aggregating function; second, according to the dynamic order; and third, according to time-delay implementation within neural units.

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals

This paper presents a novel approach to complex classification of heart abnormalities registered by electrocardiogram signals. It uses a combined approach of a Fast Fourier Technique for signal filtering and R-peaks detection and heart rate extraction, followed by signal modelling and classification by neural network based on recording of ECG. Obtained information is processed together for a complex evaluation of the signal in time.

Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction.

This paper presents a predictive model for the prediction and modeling of nonlinear, chaotic, and non-stationary electrocardiogram signals. The model is based on the combined usage of Hilbert-Huang transform, False nearest neighbors, and a novel neural network architecture. This model is intended to increase the prediction accuracy by applying the Empirical Mode Decomposition over a signal, and to reconstruct the signal by adding each calculated Intrinsic Mode Function and its residue. The Intrinsic Mode Function that obtains the highest frequency oscillation is not considered during the reconstruction. The optimal embedding dimension space of the reconstructed signal is obtained by False Nearest Neighbors algorithm. Finally, for the prediction horizon, a neural network retraining technique is applied to the reconstructed signal. The method has been validated using the record 103 from MIT-BIH arrhythmia database. Results are very promising since the measured root mean squared errors are 0.031, 0.05, and 0.085 of the ECG amplitude, for the prediction horizons of 0.0028, 0.0056, 0.0083 seconds, respectively.

Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transformHilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches.

Hilbert transform and neural networks for identification and modeling of ECG complex

This paper presents a method for modeling and identification of electrocardiogram signals; the proposed method consists of two phases; the first one is focused on obtaining the period of an ECG signal using a procedure of autocorrelation. The second phase obtains R-peaks using the Hilbert transform. Finally, an Artificial Neural Network using a retraining technique is applied for the prediction stage; this has been validated using the record 100 from the MIT-BIH arrhythmia database. Results confirm that the presented approach for detection of the ECG complex obtains 100% accuracy. The performance of the prediction method is promising due to the root mean squared errors of the prediction are of 0.029, 0.04, and 0.059 of the ECG amplitude, for 1, 2, and 3 steps ahead, respectively.

A mathematical model of the MT 25 microtron

This paper presents the design of a mathematical model developed for the set up of the control system of the MT 25 microtron, which is a cyclic electron accelerator. This type of accelerator has been controlled manually until now. The mathematical model is based on calculations of the electron motion in the accelerating cavity and vacuum chamber. The simulation diagram was created in Matlab-Simulink.