Adaptive neuro-fuzzy inference systems (ANFIS) are one of the most popular types of fuzzy neural networks. An usual approach to the synthesis of ANFIS networks is based on clustering a training set of numerical examples of the unknown mapping to be approximated. Several different clustering procedures can be adopted for this purpose, but most of them are affected by serious drawbacks. We propose a novel clustering approach in order to overcome these problems. It determines directly the consequent part of ANFIS rules; successively, the fuzzy antecedent part of each rule is determined by using a Min-Max classifier. The resulting ANFIS architecture is optimized by means of a constructive procedure, which we further propose in the paper. It allows us to determine automatically the optimal number of rules by applying well-known results of learning theory. Simulation tests and comparison with other techniques are discussed in order to prove the validity of the proposed approach.

Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

Density estimation based on a mixture of Gaussian components is particularly, suited to the solution of function approximation problems. When dealing with numerical examples of the function to be approximated, the corresponding neural network architecture can be trained by using a clustering procedure based on the well-known EM algorithm. However, the latter is characterized by some serious drawbacks that we overcome in this paper, For instance, we propose a constructive procedure that increases progressively, the number of Gaussian components; it yields improvements of both the speed and the quality of the EM convergence. Moreover, it also drastically reduces the computational cost of the optimization procedure that we further propose in order to select automatically the optimal number of Gaussian components of the neural network. The performance of the proposed approach is compared in the paper with respect to well-known neural network approaches

This work aims to analyze the impact of technological eco-innovation on the modernization and development of a local area. The role of eco-innovation would be to stimulate an innovative environment and spur a development of the territory and economic districts, and the diffusion of said particularities among wider geographic contexts, hence allowing a globalization model more observant of local specificities, and thus an open system able to develop economic and cultural exchange respecting local particularities. In recent years, smart city has asserted itself as a general model for the city of tomorrow, and sustainability has become a focal point in urban development policies. In this paper, we investigate how an integrated and intermodal methodology for the development of smart mobility systems—the European project “Life for Silver Coast”—is impacting the modernization and development of an Italian coastal area in Tuscany. The main focus of our paper is to understand how an integrated mobility network allows a transition toward a sustainable form of social relationship and a new economic pattern and could represent the starting point for a spatial, relational and institutional reorganization process that would lead to a change in the production and management dynamics of the local ecosystem concerning cultural, social and economic issues.