There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

/pdf/neural-network-toolbox-user-s-guide-ll1bn7nko0.pdf

Neural Network Toolbox™ User's Guide

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

L'incidence annuelle du syndrome de Guillain-Barre est de 1,5/100000 habitants La mortalite actuelle est estimee a environ 5 % d'apres des essais therapeutiques recents, bien conduits Dix pour cent des malades gardent des sequelles motrices tres invalidantes un an apres le debut des premiers signes neurologiques La prise en charge de ces malades necessite des equipes entrainees, multidisciplinaires, pouvant pratiquer l'ensemble des therapeutiques specifiques La corticotherapie per os'ou par voie intraveineuse est inefficace Les echanges plasmatiques sont le premier traitement dont l'efficacite a ete demontree par rapport a un groupe controle Les indications sont maintenant mieux connues Les formes benignes (marche possible) beneficient de 2 echanges plasmatiques; 2 echanges supplementaires sont realises en cas d'aggravation Dans les formes intermediaires (marche impossible) et les formes severes (recours a la ventilation mecanique), 4 echanges plasmatiques sont conseilles Il n'est pas utile d'augmenter leur nombre dans les formes severes ou en cas d'absence d'amelioration De fortes doses d'immunoglobulines donnees par voie intraveineuse (lq IV) [0,4 g/kg/j pendant 5 jours] sont aussi efficaces que les echanges plasmatiques dans les formes intermediaires et severes Dans ces formes, le choix entre Ig IV et echanges plasmatiques depend des contre-indications respectives de ces traitements et de leur faisabilite Les travaux en cours ont comme objectif de mieux preciser les indications respectives des echanges plasmatiques et des lq IV dans des formes de gravite differente, leur morbidite comparee, la dose optimale des lq IV

[Guillain-Barré syndrome].

The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights. >

/pdf/training-feedforward-networks-with-the-marquardt-algorithm-e1r0710f99.pdf

Training feedforward networks with the Marquardt algorithm

This paper proposes Fractional-order Hopfield Neural Networks (FHNN). This network is mainly based on the classic well-known Hopfield net in which fractance components with fractional order derivatives, replace capacitors. Stability of FHNN is fully investigated through energy-like function analysis. To show how effective the FHNN network is, an illustrative example for parameter estimation problem of the second-order system is finally considered in the paper. The results of simulation are very promising.

Fractional-order hopfield neural networks

Dynamic average consensus via nonlinear protocols

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

Mohammad Bagher Menhaj

Papers

Training feedforward networks with the Marquardt algorithm

Fractional-order hopfield neural networks

Dynamic average consensus via nonlinear protocols

A new fuzzy logic filter for image enhancement

Robust nonlinear model predictive control for nuclear power plants in load following operations with bounded xenon oscillations