There is a special reason for reviewing this book at this time: it is the 50th edition of a compendium that is known and used frequently in most chemical and physical laboratories in many parts of the world. Surely, a publication that has been published for 56 years, withstanding the vagaries of science in this century, must have had something to offer. There is another reason: while the book is a standard fixture in most chemical and physical laboratories, including those in medical centers, it is not as frequently seen in the laboratories of physician's offices (those either in solo or group practice). I believe that the Handbook can be useful in those laboratories. One of the reasons, among others, is that the various basic items of information it offers may be helpful in new tests, either physical or chemical, which are continuously being published. The basic information may relate

Handbook of Chemistry and Physics

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

Graphical abstractDisplay Omitted HighlightsThe comprehensive review of Krill Herd Algorithm as applied to different domain is presented.The review covers the applications, modifications and hybridizations of the KH algorithms.It provides future research directions across different areas. Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed.

A comprehensive review

Electroencephalography (EEG) is an important tool for studying the human brain activity and epileptic processes in particular. EEG signals provide important information about epileptogenic networks that must be analyzed and understood before the initiation of therapeutic procedures. Very small variations in EEG signals depict a definite type of brain abnormality. The challenge is to design and develop signal processing algorithms which extract this subtle information and use it for diagnosis, monitoring and treatment of patients with epilepsy. This paper presents a review of wavelet techniques for computer-aided seizure detection and epilepsy diagnosis with an emphasis on research reported during the past decade. A multiparadigm approach based on the integration of wavelets, nonlinear dynamics and chaos theory, and neural networks advanced by Adeli and associates is the most effective method for automated EEG-based diagnosis of epilepsy.

Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.

Theories Moleculaires de la Resonance Magnetique Nucleaire Applications a la Chimie Structurale. Par G. Mavel. Pp. x + 325. (Paris: Dunod, 1966.) 78 francs.

High Resolution NMR

In this paper a modification on Levenberg-Marquardt algorithm for MLP neural network learning is proposed. The proposed algorithm has good convergence. This method reduces the amount of oscillation in learning procedure. An example is given to show usefulness of this method. Finally a simulation verifies the results of proposed method. Keywords—Levenberg-Marquardt, modification, neural network, variable learning rate.

Modified Levenberg-Marquardt Method for Neural Networks Training

A neural network controller for load following operation of nuclear reactors

Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

This paper focuses on the optimal structural design of a hybrid permanent-magnet-electro-magnetic suspension system (PEMS) for a magnetic levitation (Maglev) transportation system in order to decrease the suspension power loss. First, the nonlinear magnetic force expression of a PEMS system is obtained by developing the magnetic equivalent circuit of the hybrid structure. The proposed analytical framework accounts for leakage fluxes and material properties such as iron reluctances. A number of design considerations are also presented to attain more practical results. Genetic algorithm is then employed to optimize the lifting force while reducing the system power loss. Moreover, 3-D finite element method (FEM) is utilized in the analyses and it is shown that the results calculated from the proposed model match well with those obtained from FEM. In addition, superiorities of the implemented model over the existing approaches are demonstrated. The outcomes show that the proposed method has increased the magnetic force, while significantly reducing the suspension power loss compared with those in the conventional pure electromagnet structure and in a previously proposed hybrid structure.

Amir Abolfazl Suratgar

Papers

Modified Levenberg-Marquardt Method for Neural Networks Training

A neural network controller for load following operation of nuclear reactors

Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

Characteristics Optimization of the Maglev Train Hybrid Suspension System Using Genetic Algorithm

Stable ANFIS2 for Nonlinear System Identification