This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf/

Artificial neural networks in hardware: A survey of two decades of progress

In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Analog hardware architecture of a memristor bridge synapse-based multilayer neural network and its learning scheme is proposed. The use of memristor bridge synapse in the proposed architecture solves one of the major problems, regarding nonvolatile weight storage in analog neural network implementations. To compensate for the spatial nonuniformity and nonideal response of the memristor bridge synapse, a modified chip-in-the-loop learning scheme suitable for the proposed neural network architecture is also proposed. In the proposed method, the initial learning is conducted in software, and the behavior of the software-trained network is learned by the hardware network by learning each of the single-layered neurons of the network independently. The forward calculation of the single-layered neuron learning is implemented on circuit hardware, and followed by a weight updating phase assisted by a host computer. Unlike conventional chip-in-the-loop learning, the need for the readout of synaptic weights for calculating weight updates in each epoch is eliminated by virtue of the memristor bridge synapse and the proposed learning scheme. The hardware architecture along with the successful implementation of proposed learning on a three-bit parity network, and on a car detection network is also presented.

Memristor Bridge Synapse-Based Neural Network and Its Learning

https://repositorio-aberto.up.pt/bitstream/10216/103038/3/68419.1.pdf

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

http://cee.uma.pt/morgado/Down/EAAI_NN_hardware070804_IFAC.pdf

Artificial neural networks: a review of commercial hardware

– The techniques of artificial intelligence based in fuzzy logic and neural networks are many times applied together. The reasons to combine these two paradigms come out of the difficulties and inherent limitations of each isolated paradigm. Generically, when they are used in a combined way, they are called Neuro-Fuzzy Systems. This term, however, is many times used to assign a specific type of system that integrates both techniques. This type of system is characterised for a fuzzy system where fuzzy sets and fuzzy rules are adjusted using input output patterns. There are several different implementations of neuro-suzzy systems, therefore each author defined its own model. This article summarizes a general vision of the area describing the most known hybrid neuro-fuzzy techniques, its advantages and disadvantages.

/pdf/neuro-fuzzy-systems-a-survey-23iqv8gon3.pdf

Neuro-Fuzzy Systems: A Survey

http://cee.uma.pt/morgado/Down/sdarticle.pdf

Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study

This work deals with the design and implementation prototype of a real time maximum power point tracker (MPPT) for photovoltaic panel (PV), aiming to improve energy transfer efficiency. This paper describes also the charging process of lead- acid batteries integrating the MPPT algorithm making an charging autonomous system that can be used to feed any autonomous application. The photovoltaic system exhibits a non-linear i-v characteristic and its maximum power point varies with solar insolation and temperature. To control the maximum transfer power from a PV panel the Perturbation and Observation (P&O) MPPT algorithm is executed by a simple microcontroller ATMEL ATTINY861V using the PV voltage and current information and controlling the duty cycle of a pulse width modulation (PWM) signal applied in to a DC/DC converter. The schematic and design of the single-ended primary inductance converter (SEPIC) is presented. This DC/DC converter is chosen because the input voltage can be higher or lower than the output voltage witch presents obvious design advantages. With the P&O MPPT algorithm implemented and executed by the microcontroller, the different charging stages of a lead-acid battery are described, showed and executed Finally, experimental results of the performance of the designed P&O MPPT algorithm are presented and compared with the results achieved with the direct connection of the PV panel to the battery.

/pdf/maximum-power-point-tracker-applied-in-batteries-charging-12bfffacxe.pdf

Maximum power point tracker applied in batteries charging with PV panels

This paper presents a efficient stand-alone battery photovoltaic (PV) lighting system which can provide functional illumination based on power light-emitting diodes (PLEDs). PLED with specific features of small size, long life and high-brightness light will be choices of future light sources. The PLEDs are feed using a lead acid battery that is charged with a PV panel. This paper presents an interface board with a ATMEL ATTINY861V microcontroller, a single-ended primary inductance converter (SEPIC) and input and output voltage and current measurements. The microcontroller runs the perturbation and observation (P&O) maximum power point tracker (MPPT) algorithm used in battery charging process. This algorithm makes the system more efficient. In order to control the PLEDs current, an equal interface board is used running now the proportional integral (PI) current PLED control algorithm adjusting the wanted level of light in the PLEDs array. The two identical interfaces boards based in the economic microprocessor achieved very good results in battery charging and discharge supervision improving the efficiency a life-time of the lead acid batteries (first board) and presents good current control results in the PLEDs array (second equal board) providing a constant PLEDs light even in voltage battery variations or discharges. The stand alone lighting system monitors the surround area and if it feels any movement it changes the level of light of PLEDs from signal light to illumination light. After a pre-defined time it comes back to signal light, saving energy.

/pdf/implementation-of-a-stand-alone-photovoltaic-lighting-system-1bx8otiw7k.pdf

Alexandre Mota

Papers

Artificial neural networks: a review of commercial hardware

Neuro-Fuzzy Systems: A Survey

Artificial neural networks and neuro-fuzzy systems for modelling and controlling real systems: a comparative study

Maximum power point tracker applied in batteries charging with PV panels

Implementation of a stand-alone photovoltaic lighting system with MPPT battery charging and LED current control