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Since complex interval matrix have many applications in different fields of science, in this paper interval complex matrix system as [W][Z] = [K] in which [W]; [K] are n × n known interval complex matrices and [Z] is n × n unknown interval complex matrix is studied. Using operations on interval complex numbers and matrices and defining a theorem, two auxiliary addition and subtraction complex systems are introduced and proved. Then, using the equality property of two complex numbers, the auxiliary interval complex systems are transformed to real crisp systems. Then the new system is solved and [Z] is achieved. Finally, some numerical examples are given to illustrate the applicability and ability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose three novel algorithms such as Novel genetic algorithm complex-valued backpropagation neural network (GA-CVBNN), Novel elegant fuzzy genetic algorithm (EFGA) and elegant fuzzy genetic algorithm-based complex-valued backpropagation neural network (EFGA-CVBNN) for classification of accuracy in datasets. In GA-CVBNN, classical Genetic Algorithm has been used for selecting appropriate initial weights for CVBNN. The EFGA is developed to resolve the drawback of classical GA by employing fuzzy logic to control parameters and selective pressure of GA. The EFGA uses a Min-Heap data structure and Pareto principle to improve the classical genetic algorithm. The EFGA-CVBNN resolves the drawbacks of classical CVBNN by employing EFGA at the time of initial weight selection. From the simulation result, the GA-CVBNN performs better than existing CVBNN and it is not efficient. To enhance the performance of GA-CVBNN, we have developed EFGA-CVBNN. Experimental results on various synthetic datasets and benchmark datasets taken from UCI machine learning repository shows that EFGA-CVBNN outperforms PSO-CVBNN in terms of classification accuracy and time. Statistical t test has been used to validate the obtained results. [ABSTRACT FROM AUTHOR]

Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better groups in the current diversity prediction theory. Diversity may hurt civic life and introduce distrust. This is because the current diversity prediction theory is based on real numbers that ignore individual abilities. Its diversity prediction theory maximizes performance with infinite population size. Contrary to this, collective intelligence or swarm intelligence is not maximized by infinite population size, but by population size. The extended diversity prediction theory using the complex number allows us to express individual abilities or qualities. The diversity of complex numbers always produces better groups and societies. The wisdom of crowds, collective intelligence, swarm intelligence or nature-inspired intelligence is implemented in the current machine learning or artificial intelligence, called Random Forest. The problem of the current diversity prediction theory is detailed in this paper. • Diversity is supposed to create better groups and societies but sometimes fails. • Diversity in real numbers ignores individual abilities. • Diversity of population size plays a key role in collective intelligence. • Diversity in complex numbers allows us to express individual abilities. • Diversity in complex numbers always produces better groups and societies. [ABSTRACT FROM AUTHOR]

It has been reported that training deep neural networks is more difficult than training shallow neural networks. Hinton et al. proposed deep belief networks with a learning algorithm that trains one layer at a time. A much better generalization can be achieved when pre‐training each layer with an unsupervised learning algorithm. Since then, deep neural networks have been extensively studied. On the other hand, it has been revealed that singular points affect the training dynamics of the learning models such as neural networks and cause a standstill of training. Naturally, training deep neural networks suffer singular points. As described in this paper, we present a deep neural network model that has fewer singular points than the usual one. First, we demonstrate that some singular points in the deep real‐valued neural network, which is equivalent to a deep complex‐valued neural network, have been resolved as its inherent property. Such deep neural networks are less likely to become trapped in local minima or plateaus caused by critical points. Results of experiments on the two spirals problem, which has an extreme nonlinearity, support our theory. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, it is presented two math teachers' discourse analysis results, to identify the possible incidence of the metaphorical language used by them, in the learning of the concept of a complex number with ninth grade students. The analysis of the metaphorical language of the teacher is done in the light of the Cognitive Theory of Mathematics. It seeks the understanding of mathematics in the metaphorical thinking of people, and not in the rigorous demonstrations of books, emphasizing the connection that has the body with the cognitive processes, inducing the main key for the reform of teaching and learning processes focused on mathematical communication. [ABSTRACT FROM AUTHOR]

The paper presents results of the work of an international group of students in the frames of the realization of the research net project “Mathematical mosaic”. The results are obtained during investigations applying software products GeoGebra, Geometer's Sketchpad and Maple. The method of the complex numbers is used for the proofs of the assertions. [ABSTRACT FROM AUTHOR]

Non-destructive testing has been used widely for the integrity testing of piles, especially low-strain testing methods, for example the sonic echo test, the impulse response method, etc. The length of a long pile is often difficult to evaluate correctly due to the fact that the energy of a reflected stress wave generally fades with the wave travel path during testing. The purpose of this paper is to use the three-dimensional amplitude and phase angle message of a complex continuous wavelet transform to determine the length of piles by analysing a time-frequency phase angle diagram. Five piles of different lengths were tested to verify the new approach used in this study. The testing results show that a complex continuous wavelet transform is not only able to provide high-resolution results in different frequency bands but can also simplify the identification of the refection point using the three-dimensional phase angle spectrum. The pile length and pile tip condition can be easily determined using a phase angle diagram under the corresponding frequency of maximum amplitude in the three-dimensional amplitude spectrum. [ABSTRACT FROM AUTHOR]

This paper explains a method of constructing algebras, starting with the properties of discrimination in elementary discrete systems. We show how to use points of view about these systems to construct what we call iterant algebras and how these algebras naturally give rise to the complex numbers, Clifford algebras and matrix algebras. The paper discusses the structure of the Schrödinger equation, the Dirac equation and the Majorana Dirac equations, finding solutions via the nilpotent method initiated by Peter Rowlands. [ABSTRACT FROM AUTHOR]

The students learn about complex numbers and Euclidean plane geometry in high school. Nevertheless, this material is separated into units instead of being integrated. (Anevska, 2014) presents methodological aspects about the study of exponential presentation of the complex numbers and (Anevska & al.2015-1) presents the possibility for inter-subject integration of the mathematics instruction in the study of the mentioned topics. Further on, (Anevska & al., 2015-2) and (Anevska & al., 2016) present the results of the comparative analyses of the scores of the students regarding the transformations in the Euclidean plane and the metric characteristics of the geometric figures and their use when studied in the standard way (see (Mitrović & al., 1998)), and when studied with the use of complex numbers (see (Malcheski & al., 2015)). This paper offers analogous analyses regarding the acquired knowledge in these two ways when solving constructive tasks. [ABSTRACT FROM AUTHOR]

The study of Euclidean plane geometry is compulsory for high school students. Practice shows that even the most advanced students face diffi culties when learning this material, and especially the content related to the metric characteristics of the geometric fi gures and their use. On the other hand, the high school syllabus also includes complex numbers, characterized by an analytical apparatus, which is adequate for learning Euclidean plane geometry. This paper presents the results of a comparative analysis of the scores of the students related to the metric characteristics of the geometric fi gures in the Euclidean plane and their use, when studied with the use of complex numbers (Malcheski, Grozdev & Anevska, 2015), and when studied in a standard way (Mitrović & al., 1998). [ABSTRACT FROM AUTHOR]

This paper introduces one unique price motion process with fractional Brownian motion. We introduce the imaginary number into the agent's subjective probability for the reason of convergence; further, the result similar to Ito Lemma is proved. As an application, this result is applied to Merton's dynamic asset pricing framework. We find that the four order moment of fractional Brownian motion is entered into the agent's decision-making. The decomposition of variance of economic indexes supports the possibility of the complex number in price movement. [ABSTRACT FROM AUTHOR]

Adopting transformations in the Euclidean plane and their application is compulsory for high school students. The practice shows that even advanced students face diffi culties when learning such a material, especially the part which refers to group properties. On the other hand, complex numbers are also studied in high school and they are characterized by an analytical apparatus which helps learning the transformations in the Euclidean plane. This paper presents results from a comparative analysis referring to the accomplishments of the students who study transformations in the Euclidean plane and their application by using complex numbers (see (Malcheski et al., 2015)) and in the classical way (see (Mitrović et al., 1998)). [ABSTRACT FROM AUTHOR]

Binarized neural networks, or BNNs, show great promise in edge-side applications with resource limited hardware, but raise the concerns of reduced accuracy. Motivated by the complex neural networks, in this paper we introduce complex representation into the BNNs and propose Binary complex neural network – a novel network design that processes binary complex inputs and weights through complex convolution, but still can harvest the extraordinary computation efficiency of BNNs. To ensure fast convergence rate, we propose novel BCNN based batch normalization and weight initialization strategies. Experimental results on image and radio signal classifications show that BCNN can achieve better accuracy compared to the original BNN models. BCNN improves BNN by strengthening its learning capability through complex representation and extending its applicability to complex-valued input data. Our code is available at https://github.com/flying-Yan/BCNN. [ABSTRACT FROM AUTHOR]