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Despite their excellent performance and versatility, the efficient integration of small lasers with other optical devices has long been hindered by their broad emission divergence. In this study, we introduce a novel approach for emission engineering in microdisk lasers, significantly enhancing their vertical emission output by directly integrating specially designed reflective metalenses, referred to as “meta-micromirrors”. A 5 μm-diameter microdisk laser is precisely positioned at an 8 μm focal distance on a 30 × 30 μm2 meta-micromirror featuring a numerical aperture (NA) of 0.95, accomplished through micro-transfer printing techniques. Our experiments demonstrated a notable increase in the emission efficiency within an NA of 0.65. Specifically, we observed a 2.68-fold increase in the average emission from ten microdisk lasers. This integration not only enhances the emission efficiency of small lasers but also holds considerable implications for micro- and nano-photonic integrations. The results of this integration open up new possibilities in various fields, including photonic integrated circuits, bio-sensing technologies, and the development of quantum light sources.

In this paper, we develop an advanced architecture and come up with a comprehensive design methodology of genetically optimized hybrid fuzzy neural networks (gHFNNs). The construction of gHFNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNN results from a highly synergistic usage of the genetic optimization-driven hybrid system being generated by combining fuzzy neural networks (FNNs) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. The optimization of the FNN is realized with the aid of a standard backpropagation learning algorithm and genetic optimization. As the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method-based learning (optimization). Through the consecutive process of such structural and parametric optimization, an optimized PNN becomes generated in a dynamic fashion. To evaluate the performance of the gHFNNs, we experimented with a number of representative numerical examples. A comparative analysis demonstrates that the proposed gHFNNs are neurofuzzy systems with higher accuracy as well as more superb predictive capability than other models available in the literature. Index Terms--Computational intelligence (CI), design procedure, fuzzy neural networks (FNNs), genetically optimized hybrid fuzzy neural networks (gHFNNs), genetically optimized polynomial neural networks (gPNNs), group method of data handling (GMDH).

In this study, we introduce and investigate a class of neural architectures of Polynomial Neural Networks (PNNs), discuss a comprehensive design methodology and carry out a series of numeric experiments. Two kinds of PNN architectures, namely a basic PNN and a modified PNN architecture are discussed. Each of them comes with two types such as the generic and the advanced type. The essence of the design procedure dwells on the Group Method of Data Handling. PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but becomes dynamically meaning that the network grows over the training period. In this sense, PNN is a self-organizing network. A comparative analysis shows that the proposed PNN are models with higher accuracy than other fuzzy models. Keywords: Polynomial neural networks (PNNs); The generic and the advanced type of PNNs; Group method of data handling (GMDH); Self-organizing network; Design procedure

Experimental software datasets describing software projects in terms of their complexity and development time have been a subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such development frameworks as neural networks, fuzzy and neurofuzzy models. In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining neurofuzzy networks (NFN) and polynomial neural networks (PNN). For these networks we develop a comprehensive design methodology. The construction of SONFNs takes advantage of the we!l-established technologies of computational intelligence (CI), namely fuzzy sets, neural networks and genetic algorithms. The architecture of the SONFN results from a synergistic usage of NFNs and PNNs. NFN contributes to the formation of the premise part of the rule-based structure of the SONFN. The consequence part of the SONFN is designed using PNNs. We discuss two types of SONFN architectures whose taxonomy is based on the NFN scheme being applied to the premise part of SONFN. We introduce a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SONFN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). The experimental results include a well-known NASA dataset concerning software cost estimation. Index Terms--Computational intelligence, genetic algorithms, linear fuzzy inference, neurofuzzy networks, polynomial neural networks, self-organizing neurofuzzy networks.