A deep learning integrated Lee-Carter model

In the field of mortality, the Lee–Carter based approach can be considered the milestone to forecast mortality rates among stochastic models. We could define a “Lee–Carter model family” that embraces all developments of this model, including its first formulation (1992) that remains the benchmark for comparing the performance of future models. In the Lee–Carter model, the kt parameter, describing the mortality trend over time, plays an important role about the future mortality behavior. The traditional ARIMA process usually used to model kt shows evident limitations to describe the future mortality shape. Concerning forecasting phase, academics should approach a more plausible way in order to think a nonlinear shape of the projected mortality rates. Therefore, we propose an alternative approach the ARIMA processes based on a deep learning technique. More precisely, in order to catch the pattern of kt series over time more accurately, we apply a Recurrent Neural Network with a Long Short-Term Memory architecture and integrate the Lee–Carter model to improve its predictive capacity. The proposed approach provides significant performance in terms of predictive accuracy and also allow for avoiding the time-chunks’ a priori selection. Indeed, it is a common practice among academics to delete the time in which the noise is overflowing or the data quality is insufficient. The strength of the Long Short-Term Memory network lies in its ability to treat this noise and adequately reproduce it into the forecasted trend, due to its own architecture enabling to take into account significant long-term patterns

Design of Input Sequence to Capture Adequate Non Linearity for Empirical Modeling Purposes

The objective of this report is to discuss the preliminary research done and basic understanding of the chosen topic, which is Design of Input Sequence to Capture Adequate Non Linearity. The ultimate aim of the project is to find the best input sequence that can capture adequate non linearity and give the best predictive empirical model of Continuous Stir Tank Reactor. The challenge in this project is to find the available input sequence, which has been available in other people research project, applied them in MATLAB Simulink and further tested in various types of Neural Network. Simulation model will be design to test for the best input sequence that will give the best result for prediction of output. Once the result from the simulation has been get, the best input sequence will be test on the real system to prove that the result obtain in the real cases is similar with simulation that had been carried out

Principles for Consciousness in Integrated Cognitive Control

In this article we will argue that given certain conditions for the evolution of bi- \ud ological controllers, these will necessarily evolve in the direction of incorporating \ud consciousness capabilities. We will also see what are the necessary mechanics for \ud the provision of these capabilities and extrapolate this vision to the world of artifi- \ud cial systems postulating seven design principles for conscious systems. This article \ud was published in the journal Neural Networks special issue on brain and conscious- \ud ness

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with feedback regulation, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organism's response to perturbations. We use examples from aquatic (electric fish station keeping and jamming avoidance), terrestrial (cockroach wall following) and aerial environments (flight control in moths) to highlight how one can use control theory to understand how feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.Comment: Submitted to Integr Comp Bio

Transformer NN-based behavioral modeling and predistortion for wideband pas

Abstract. This work investigates the suitability of transformer neural networks (NNs) for behavioral modeling and the predistortion of wideband power amplifiers. We propose an augmented real-valued time delay transformer NN (ARVTDTNN) model based on a transformer encoder that utilizes the multi-head attention mechanism. The inherent parallelized computation nature of transformers enables faster training and inference in the hardware implementation phase. Additionally, transformers have the potential to learn complex nonlinearities and long-term memory effects that will appear in future high-bandwidth power amplifiers. The experimental results based on 100 MHz LDMOS Doherty PA show that the ARVTDTNN model exhibits superior or comparable performance to the state-of-the-art models in terms of normalized mean square error (NMSE) and adjacent channel power ratio (ACPR). It improves the NMSE and ACPR up to −37.6 dB and −41.8 dB, respectively. Moreover, this approach can be considered as a generic framework to solve sequence-to-one regression problems with the transformer architecture

Model based control strategies for a class of nonlinear mechanical sub-systems

This paper presents a comparison between various control strategies for a class of mechanical actuators common in heavy-duty industry. Typical actuator components are hydraulic or pneumatic elements with static non-linearities, which are commonly referred to as Hammerstein systems. Such static non-linearities may vary in time as a function of the load and hence classical inverse-model based control strategies may deliver sub-optimal performance. This paper investigates the ability of advanced model based control strategies to satisfy a tolerance interval for position error values, overshoot and settling time specifications. Due to the presence of static non-linearity requiring changing direction of movement, control effort is also evaluated in terms of zero crossing frequency (up-down or left-right movement). Simulation and experimental data from a lab setup suggest that sliding mode control is able to improve global performance parameters

Variance-constrained multiobjective control and filtering for nonlinear stochastic systems: A survey

The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H 2 / H ∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out