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An experimental study on the influence of the computation of basic nodal nonlinear functions on the performance of (NFSs) is described in this paper. Systems' architecture size, their approximation capability, and the smoothness of provided mappings are used as performance indexes for this comparative paper. Two widely used kernel functions, the sigmoid-logistic function and the Gaussian function, are analyzed by their computation through an accuracy-controllable approximation algorithm designed for hardware implementation. Two artificial neural network (ANN) paradigms are selected for the analysis: backpropagation neural networks (BPNNs) with one hidden layer and radial basis function (RBF) networks. Extensive simulation of simple benchmark approximation problems is used in order to achieve generalizable conclusions. For the performance analysis of fuzzy systems, a functional equivalence theorem is used to extend obtained results to fuzzy inference systems (FISs). Finally, the adaptive neurofuzzy inference system (ANFIS) paradigm is used to observe the behavior of neurofuzzy systems with learning capabilities.

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A controlled accuracy approximation scheme of the sigmoid function for artificial neuron implementation based on Taylor's theorem and the Lagrange form of the error is proposed. The main advantages of the proposed solution are two: it provides a systematic way to guarantee the required accuracy and it reuses the circuitry of the linear part of the neuron to compute the sigmoid function. The sigmoid derivative is also available for artificial neural networks with online learning capabilities.

The availability of cheap wearable motion and biometric sensors has favoured the research on wearable human activity recognition (HAR) systems. However, a HAR system comprehends many complex signal processing stages that usually require some computationally demanding operations which can hardly be directly performed in an embedded system. Modern FPGA technologies and the system-on-chip (SoC) approach open the door to the implementation of complex single-chip signal processing systems to produce tiny, wearable and autonomous embedded HAR systems. However, compared to a pure embedded software approach, the potentially higher performance-to-power ratio of FPGAs can only be exploited in very demanding applications and by a careful design of the implemented system. In this work we describe a first step in the consecution of an FPGA-based completely autonomous singlechip HAR system which can be adapted and optimized to the user with no need of external computing means neither of human intervention. The system includes all stages in a HAR process, i.e., signal segmentation, signal processing for feature extraction, input space dimensionality reduction (feature selection), and activity estimation by means of a neural classifier. A physical activity recognition example is used as a reference design to evaluate the performance of the system and to draw conclusions on the potential benefits of using FPGAs in future wearable HAR applications.

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines.

Model Predictive Control (MPC) is a well establishedcontrol strategy that is being used in an increasingly wider set of application areas. Unfortunately, the requirement of an on-line solution to a constrained optimization problem is an impediment for its application to fast dynamics plants. The hardware implementation of target-optimized, application-specific controllers on reconfigurable Field Programmable Gate Arrays (FPGA) can widespread the use of MPC to control systems that demand short sampling periods. This paper describes a first approach to the design and implementation of a fixed-point arithmetic model predictive controller adapted to a benchmark control problem with constraints. A FPGA chip is a suitable hardware platform to implement the tailored controller as it allows for its adaptation to other operating requirements and to different target systems while meeting real time constraints.